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An article which discusses the limitations of a small scale study

A cross-national comparative perspective on racial inequities in health: the USA versus Canada

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An article which uses purposive or opportunistic sampling

The use of purposeful sampling in a qualitative evidence synthesis: A worked example on sexual adjustment to a cancer trajectory

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doi: 10.1186/s12874-016-0114-6.

Affiliations

¹ Mental Health and Wellbeing Research Group (MENT), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, 1050, Belgium. cbenoot@vub.ac.be.

² Centre for Sociological Research, Catholic University of Leuven, Parkstraat 45, Leuven, 3000, Belgium. karin.hannes@kuleuven.be.

Free PMC article

Free PMC article

doi: 10.1186/s12874-016-0114-6.

Affiliations

¹ Mental Health and Wellbeing Research Group (MENT), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, 1050, Belgium. cbenoot@vub.ac.be.

² Centre for Sociological Research, Catholic University of Leuven, Parkstraat 45, Leuven, 3000, Belgium. karin.hannes@kuleuven.be.

Abstract

Background: An increasing number of qualitative evidence syntheses papers are found in health care literature. Many of these syntheses use a strictly exhaustive search strategy to collect articles, mirroring the standard template developed by major review organizations such as the Cochrane and Campbell Collaboration. The hegemonic idea behind it is that non-comprehensive samples in systematic reviews may introduce selection bias. However, exhaustive sampling in a qualitative evidence synthesis has been questioned, and a more purposeful way of sampling papers has been proposed as an alternative, although there is a lack of transparency on how these purposeful sampling strategies might be applied to a qualitative evidence synthesis. We discuss in our paper why and how we used purposeful sampling in a qualitative evidence synthesis about 'sexual adjustment to a cancer trajectory', by giving a worked example.

Methods: We have chosen a mixed purposeful sampling, combining three different strategies that we considered the most consistent with our research purpose: intensity sampling, maximum variation sampling and confirming/disconfirming case sampling.

Results: The concept of purposeful sampling on the meta-level could not readily been borrowed from the logic applied in basic research projects. It also demands a considerable amount of flexibility, and is labour-intensive, which goes against the argument of many authors that using purposeful sampling provides a pragmatic solution or a short cut for researchers, compared with exhaustive sampling. Opportunities of purposeful sampling were the possible inclusion of new perspectives to the line-of-argument and the enhancement of the theoretical diversity of the papers being included, which could make the results more conceptually aligned with the synthesis purpose.

Conclusions: This paper helps researchers to make decisions related to purposeful sampling in a more systematic and transparent way. Future research could confirm or disconfirm the hypothesis of conceptual enhancement by comparing the findings of a purposefully sampled qualitative evidence synthesis with those drawing on an exhaustive sample of the literature.

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References

Walsh D, Downe S. Meta-synthesis method for qualitative research: a literature review. J Adv Nurs. 2005;50:204-211. doi: 10.1111/j.1365-2648.2005.03380.x. - DOI - PubMed

Hannes K, Macaitis K. A move to more systematic and transparent approaches in qualitative evidence synthesis: update on a review of published papers. Qual Res. 2012;12:402-442. doi: 10.1177/1468794111432992. - DOI

Finfgeld-Connett D. Metasynthesis Findings: Potential Versus Reality. Qual Health Res. 2014;24(11):1581-1591. doi: 10.1177/1049732314548878. - DOI - PubMed

Noyes J, Popay J, Pearson A, Hannes K, Booth A. Qualitative Research and Cochrane Reviews. In: Julian H, Sally G, editors. Cochrane Handbook for Systematic Reviews of Interventions. UK: Wiley Blackwell; 2008.

Hannes K, Lockwood C, editors. Synthesizing Qualitative Research. Chichester: John Wiley & Sons, Ltd; 2011.

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bibliographic / statistics & numerical data*

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health services research / statistics & numerical data*

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medical oncology / statistics & numerical data

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national center for biotechnology information

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The use of purposeful sampling in a qualitative evidence synthesis: A worked example on sexual adjustment to a cancer trajectory - PubMed

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This paper helps researchers to make decisions related to purposeful sampling in a more systematic and transparent way. Future research could confirm or disconfirm the hypothesis of conceptual enhancement by comparing the findings of a purposefully sampled qualitative evidence synthesis with those d ...

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An article which discusses the generalisabilityof the findings

Increasing business students’ Confidence in Questioning the Validity and Reliability of their Research

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Using Multiple-hierarchy Approaches to Understand Health Inequalities: The Intersecting Consequences of Race, Gender, SES, and Age

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Abstract

This study examines how the intersecting consequences of race-ethnicity, gender, socioeconomics status (SES), and age influence health inequality. We draw on multiple-hierarchy stratification and life course perspectives to address two main research questions. First, does racial-ethnic stratification of health vary by gender and/or SES? More specifically, are the joint health consequences of racial-ethnic, gender, and socioeconomic stratification additive or multiplicative? Second, does this combined inequality in health decrease, remain stable, or increase between middle and late life? We use panel data from the Health and Retirement Study ( N = 12,976) to investigate between- and within-group differences in in self-rated health among whites, blacks, and Mexican Americans. Findings indicate that the effects of racial-ethnic, gender, and SES stratification are interactive, resulting in the greatest racial-ethnic inequalities in health among women and those with higher levels of SES. Furthermore, racial-ethnic/gender/SES inequalities in health tend to decline with age. These results are broadly consistent with intersectionality and aging-as-leveler hypotheses.

Keywords: aging and life course, gender, health disparities, race-ethnicity, stratification

As key dimensions of social stratification, race- ethnicity, gender, and socioeconomic status (SES) structure differential access to resources, exposure to risks, and life chances-including health. Indeed, racial-ethnic, gender, and socioeconomic inequalities in health are hallmarks of the U.S. population's health profile. Blacks and Mexican Americans experience higher rates of morbidity than whites ( Brown, O'Rand, and Adkins 2012; Williams and Mohammed 2013). Although women have lower mortality rates than men, they often exhibit worse health ( Bird and Rieker 2008; Read and Gorman 2010). Furthermore, there is a well-established SES gradient in health: health worsens as economic disadvantage increases ( House, Lantz, and Herd 2005; Link and Phelan 1995). In addition to lost years and quality of life, these health inequalities carry significant economic costs due to excess health care spending and reduced labor market productivity ( LaVeist, Gaskin, and Richard 2011). This toll is likely to increase over the next several decades, given several population trends-for example, the aging baby boom cohort, rising life expectancies, and increasing racial-ethnic diversity of the older population. Between 2010 and 2030, the older population-the majority of whom are women-is projected to increase from 40 to 72 million, with the proportions of older blacks and Hispanics increasing by 17% and 67%, respectively ( Vincent and Velkoff 2010). These trends make it imperative that we examine the influence of race-ethnicity, gender, and SES on health inequalities in the context of aging.

While health inequalities among racial-ethnic, gender, and SES groups have been well documented independently (e.g., Link and Phelan 1995; Read and Gorman 2010; Williams and Mohammed 2013), few studies have examined how the intersection of these dimensions of stratification influences health trajectories. The conventional, unidimensional approach to studying the social stratification of health has been to examine how an individual's position in a single status hierarchy affects well-being. For example, sociologists studying racial-ethnic inequality in health infrequently consider how it may be gendered and vice versa. Even fewer studies have examined the potentially synergistic effects of race-ethnicity, gender, and SES. Intersectionality and other multiple-hierarchy stratification scholars have cogently argued that examining inequalities along racial-ethnic, gender, and SES lines separately masks their intersecting consequences and essentializes race-ethnicity and gender ( Collins 2000; Clark and Maddox 1992; Davis 1981; McCall 2005; Schulz and Mullings 2006). Furthermore, the few studies that have explored the joint health consequences of these social factors have assumed that they are additive. This assumption, while an improvement over unidimensional approaches, obscures the potentially multiplicative effects of social statuses on health inequality posited by intersectionality scholars.

Another limitation of previous research on how multiple dimensions of inequality jointly affect health has been the scant attention to the patterning of this effect as individuals age. Although age stratification and life course perspectives emphasize age as a dimension of inequality ( Dannefer 2003; Ferraro, Shippee, and Schafer 2009; O'Rand and Henretta 1999; Pavalko and Caputo 2013; Willson, Shuey, and Elder 2007), few studies have examined whether health inequalities along racial-ethnic, gender, and SES lines (and their intersections) decrease, remain stable, or increase with increasing age (for exceptions, see Ailshire and House 2011; Clark and Maddox 1992). Instead, they have tacitly assumed that the relationships among race-ethnicity, gender, SES, and health do not vary with age. If this assumption is incorrect, then the prevailing practice of ignoring how race-ethnicity, gender, SES, and age intersect has grossly limited our understanding of the dynamic nature of health inequality over time.

This study aims to challenge this assumption and extend previous research on health stratification by examining the intersecting consequences of race-ethnicity, gender, SES, and age on health inequities in middle and late life. We draw on multiple-hierarchy stratification and life course perspectives to better understand the social distribution of health over time, focusing on two main research questions. First, does racial-ethnic stratification of health vary by gender and/or class? More specifically, are the joint health consequences of racial-ethnic, gender, and socioeconomic stratification additive or multiplicative? Second, does this combined inequality in health decrease, remain stable, or increase between middle and late life?

To answer these questions, we investigated between- and within-group differences in self-rated health among non-Hispanic white, non-Hispanic black, and Mexican American men and women. By using a multiple-hierarchy approach to health stratification within a life course framework, we found that racial-ethnic inequality in self-rated health is gendered and classed. Specifically, we found that racial-ethnic health inequality is greatest among women and those with higher levels of SES. Moreover, racial-ethnic minorities-especially women of color-experience diminished heath returns to socioeconomic resources. In addition, health stratification along racial-ethnic, gender, and SES lines are dynamic over time, most often declining with age. Thus, this study provides a better understanding of the joint and long-term health consequences of racial-ethnic, gender, and SES stratification.

BACKGROUND

Socioeconomic Inequalities in Health

The association between SES and health is well established in the literature. Studies have consistently shown than health differs by social standing, with those at the lower end of the socioeconomic hierarchy-that is, with less education, income, wealth, or occupational prestige-tending to have worse health than their higher-status counterparts ( Link and Phelan 1995; Marmot 1991; Willson et al. 2007). The reason for this robust relationship is that socioeconomic factors at individual, household, and neighborhood levels structure both exposure to risk factors for poor health and access to protective factors ( Braveman et al. 2005; House et al. 2005). Beyond shaping an individual's access to tangible health-promoting resources, such as quality food and medical care, SES also influences knowledge, power, and other resources that are needed to avoid risk or the negative consequences of exposure to risk ( Link and Phelan 1995).

Racial-ethnic Inequalities in Health

Black-white inequalities in health are well documented, revealing a consistent pattern of black health disadvantage. Blacks have worse self-rated health ( Cummings and Jackson 2008; Yang and Lee 2009) and elevated rates of hypertension, diabetes, heart disease, functional limitations, and mortality compared to whites ( Farmer and Ferraro 2005; Haas and Rohlfsen 2010; Hummer et al. 2000; Pleis, Ward, and Lucas 2010). However, evidence regarding health inequality between Mexican Americans and whites is mixed. Mexican Americans report worse self-rated health; suffer from higher rates of diabetes, kidney, and liver disease; and have more functional limitations than whites ( Markides et al. 1997; Pleis et al. 2010; Shetterly et al. 1996). Conversely, Mexican Americans have lower rates of hypertension and cancer than whites and comparable rates of mortality ( Hummer et al. 2000; Pleis et al. 2010). These mixed findings regarding Mexican Americans' health may be due, on the one hand, to the large proportion of recent Mexican immigrants who have better health profiles than U.S.-born whites and Mexican Americans with longer durations of U.S. residence ( Markides et al. 2007) and, on the other hand, to the fact that. U.S.-born Mexican Americans tend to have worse health than their white counterparts ( Hayward, Warner, and Crimmins 2007).

The prevailing approach to research on the mechanisms underlying racial-ethnic inequalities in health has been to focus on racial-ethnic differences in SES, health behavior, and medical care. However, these differences do not fully account for racial-ethnic inequalities in health ( Cummings and Jackson 2008; Hayward et al. 2000). Sociologists have long recognized the possibility that an array of factors stemming from institutional and interpersonal racism contribute to health disparities ( Du Bois 1899). Indeed, there is growing evidence that residential segregation and discrimination across the life course play key roles in racial-ethnic health inequalities ( Gee and Ford 2011; Thoits 2010; Williams and Mohammed 2013).

Gender Inequalities in Health

The population health literature has long shown that health varies by gender. Although women live longer than men, they have higher morbidity rates and diminished quality of life ( Bird and Rieker 2008). Previous studies show that compared to men, women have worse self-rated health ( Idler 2003; Yang and Lee 2009), more nonfatal chronic conditions, and greater likelihood of functional limitations ( Read and Gorman 2010; Verbrugge 1985). These differences may be partially due to gendered social roles and expectations ( Bird and Rieker 2008). For example, scholars have noted the effects of hegemonic masculinity on men's health behaviors-for example, risk taking, substance abuse, and avoidance of medical care-which may help explain their shorter life expectancy despite lower morbidity rates ( Courtenay 2000). More commonly, the poorer health of women relative to men has been attributed to women's lower SES, though differences in SES do not fully explain the gender gap in health ( Read and Gorman 2010). Thus, recent scholarship on gender inequalities in health theorizes that women's health disadvantage stems from women's greater stress exposure, as well as their constrained choices, due to institutional and family arrangements ( Bird and Rieker 2008; Thoits 2010).

Multiple-hierarchy Stratification Perspectives on Health Inequality

Although racial-ethnic, gender, and socioeconomic inequalities in health are well documented, little is known about how the combination of these dimensions of stratification (and others) shapes health. Filling this gap requires application of the multiple-hierarchy stratification perspective ( Clark and Maddox 1992; Grollman 2014; Jeffries and Ransford 1980; Schieman and Plickert 2007), which seeks to understand how ascribed and achieved statuses interact over the life course to shape life chances, including health. This perspective, therefore, reflects one of the most central tenets of sociology-that is, that life chances are shaped by a constellation of social factors ( Weber [1922] 1946). This study focuses on the intersecting consequences of race-ethnicity, gender, and SES, given their well-established and robust associations with health. Moreover, these three statuses are regarded as the "holy trinity of stratification" and are readily observed in society and used by others to evaluate individuals ( Massey 2007). Below we consider two alternative multiple-hierarchy stratification perspectives: multiple jeopardy and intersectionality. Each of these frameworks is informed by black feminism ( Collins 2000) and critical race feminist theories ( Few 2007) and generates different hypotheses about how race-ethnicity, gender, and SES combine to stratify health outcomes.

The multiple jeopardy hypothesis predicts that as a consequence of the additive effects of race-ethnicity, gender, and SES, white men of high SES would enjoy the best health, low-SES black and Mexican American women would experience the worst health outcomes, and other race/gender/SES groups would fall somewhere in between these extremes. In other words, the health disadvantages experienced by poor black and Mexican American women are the sum of those associated with being poor, those associated with being a woman, and those associated with being black/Mexican American ( Beal 1970). Such additive models have been criticized for failing to consider pertinent differences in the contexts within which white versus nonwhite women experience sexism, minority men versus minority women experience racism, and all racial-ethnic/gender groups experience capitalism ( Weber 2010). It is, therefore, limited in its ability to capture multiple-hierarchy stratification because it reinforces the notion that these social statuses are independent dimensions of stratification ( King 1988).

The intersectionality hypothesis responds to this critique of the multiple jeopardy hypothesis by positing that black and Mexican American women are likely to be especially disadvantaged because dimensions of inequality interact in multiplicative ways, resulting in unique social contexts that condition the lived experiences and life chances of the individuals situated within those contexts ( Collins 2000; Choo and Ferree 2010; Hinze, Lin, and Andersson 2012). In other words, a key premise of intersectionality is that racial-ethnic, gender, and SES stratification are both simultaneous and interactive (Ailshire and House 2000; Brown and Hargrove 2013; Landry 2007), that is, "interlocking" ( Dill and Zambrana 2009). Thus, rather than examining the effects of different social statuses separately, or assuming that the total disadvantage experienced by people with multiple disadvantaged statuses is equivalent to the sum of their disadvantages, intersectionality highlights the importance of examining the multiplicative effects of several social statuses on health ( Choo and Ferree 2010; Veenstra 2013). Thus, an intersectionality hypothesis would predict that race-ethnicity, gender, and SES have nonadditive effects on health such that the health consequences of each of these social status dimensions are conditional upon each other. For example, it would predict that the magnitude of racial-ethnic health gaps will be greater among women than men, due to the compounding effects of racial-ethnic and gender inequality ( Brown and Hargrove 2013). Similarly, an intersectionality perspective would predict that the SES-health gradient will be less steep among racial-ethnic minorities, women, and especially, minority women ( Jackson and Williams 2006). Moreover, white men with high SES would be expected to have the best health, while black and Mexican American women with low SES would have the worst health, and other race-ethnicity/gender/SES groups would occupy liminal statuses ( Schulz and Mullings 2006).

Evidence of Multiple-hierarchy Stratification of Health

Relatively few empirical studies have examined the combined effects of racial-ethnic, gender, and SES inequality on health. Among those that have, most have not explicitly tested whether their effects are additive or multiplicative. Rather, they have tended to use a "configurational" approach ( Alon 2007), which typically compares race-ethnicity/gender combinations (e.g., black women, black men, white women) to one reference group (usually white men). Previous research using this approach has found that black and Mexican American women are disadvantaged relative to other racial-ethnic/gender groups in terms of health ( Geronimus et al. 2007; Read and Gorman 2006; Warner and Brown 2011).

Of the few studies that have investigated whether the effects of social factors are additive or multiplicative, most have examined interactions between only two social statuses, have focused exclusively on blacks and whites, and have yielded mixed results. On the one hand, several studies appear to support the multiple jeopardy hypothesis. Hinze and colleagues (2012), for example, showed that the joint consequences of race and gender on self-rated health were additive among black and white adults with at least a high school education. In addition, Hayward and colleagues (2000) found no evidence of interactions between race and socioeconomic resources on prevalence of chronic conditions among older blacks and whites. On the other hand, findings from several studies indicated that race-ethnicity and gender combine in a multiplicative fashion, leading to greater racial-ethnic inequality in health among women than among men-consistent with the intersectionality hypothesis ( Brown and Hargrove 2013; Hayward et al. 2000; Umberson et al. 2014). Moreover, Farmer and Ferraro (2005) found that race and education interact, resulting in a steeper education-health gradient among whites than among blacks and the largest black-white gap in health at higher levels of education. Furthermore, a study by Cummings and Jackson (2008) found evidence of an interaction between race-gender configurational position and educational attainment, which indicated that black women experience diminished self-rated health returns on education, relative to white men and women.

Due to these mixed findings and gaps in the literature, it remains unclear how race-ethnicity, gender, and SES combine to shape health outcomes. We aim to fill these gaps in the literature by testing the multiple jeopardy and intersectionality hypotheses. Based on the weight of the theoretical and empirical evidence discussed above, we hypothesize that the magnitude of racial-ethnic differences in health is greater among women than among men and greater among high-SES than among low-SES adults, leading black and Mexican American women with low SES to experience especially poor health. This hypothesis is indicative of interactive effects of race-ethnicity, gender, and SES on health, consistent with the intersectionality perspective.

Combining Multiple-hierarchy Stratification and Life Course Perspectives to Study Health Inequality

In addition to examining health inequality along racial-ethnic, gender, and SES lines, it is also important to consider whether and how the social stratification of health varies with age. Although age is recognized as a dimension of stratification ( Riley, Johnson, and Foner 1972), there is debate in the health inequalities literature (and in inequality research writ large) about whether inequality decreases, remains stable, or increases with age ( O'Rand and Henretta 1999). The aging-as-leveler hypothesis asserts that health inequality should decline with advancing age, leading to converging health trajectories, particularly after midlife ( Brown et al. 2012; House et al. 2005). Two of the primary processes that result in leveling include (1) mortality selection, which leads to the most disadvantaged (and least healthy) groups being selected out of the sample, and (2) delayed onset and progression of illness due to the social and economic resources that advantaged groups possess, which effectively compresses their morbidity to later life, at which point they experience more rapid declines than disadvantaged groups. Both of these processes are likely to contribute to declining health inequalities at later ages ( Dupre 2007). In the context of intersectionality, for example, the aging-as-leveler hypothesis would predict that racial-ethnic, gender, and SES inequality, as well as the multiplicative deleterious consequences associated with being a nonwhite woman of low SES, would decrease between middle and late life. Alternatively, the persistent inequality hypothesis posits that intracohort gaps in health are stable across the life course ( Ferraro and Farmer 1996; Henretta and Campbell 1976). Thus, it predicts that health advantages and disadvantages will hold, with age neither equalizing nor amplifying the difference. Finally, the cumulative disadvantage hypothesis predicts that health trajectories of advantaged and disadvantaged individuals widen with age ( Dannefer 2003; Pavalko and Caputo 2013). Individuals with initial advantages accumulate more resources and opportunities over time and are better able to avoid or allay health risks; disadvantages in early life precipitate subsequent disadvantages and risks as individuals age ( Ferraro et al. 2009; Willson et al. 2007). In the case of intersectionality, the cumulative disadvantage hypothesis would predict that health inequalities along racial-ethnic, gender, and SES lines (and their intersections) would increase between middle and late life. Indeed, some evidence suggests that black women experience accelerated health deterioration or "weathering" with advancing age ( Geronimus et al. 2007).

Due to limitations of previous studies, it is unclear which of these hypotheses apply to life course patterns of gendered and classed racial-ethnic health inequalities. First, few longitudinal studies of health inequalities have applied a multiple-hierarchy approach to racial-ethnic, gender, and class stratification. Rather, longitudinal health studies have largely analyzed the independent effects of race-ethnicity ( Brown et al. 2012; Kim & Miech, 2009), gender ( Anderson et al. 1998; Liang et al. 2008), or SES ( Lantz et al. 2001; Ross and Wu 1996), and these unidimensional studies have yielded mixed evidence for the alternate life course hypotheses. Second, given that age inequality dynamics are likely to vary across life stages (e.g., early life, midlife, and late life; House et al. 2005), inconsistent support for the alternate life course hypotheses may be due to the fact that previous studies have tended to examine them using different age ranges. For these reasons, it remains unknown whether and how the simultaneity and interaction of race-ethnicity, gender, and SES advantages or disadvantages particular subgroups with respect to their health over the life course. There is, however, growing evidence that health inequality along single dimensions of stratification tends to peak in midlife and wane at advanced ages ( Brown et al. 2012; Haas and Rohlfsen 2010; Shuey and Willson 2008; Willson et al. 2007). Although advantaged groups are likely to have better health than disadvantaged groups over much of the life course, they are disproportionately affected by the deleterious consequences of aging in later life, resulting in narrowing health gaps ( House et al. 2005). Therefore, we hypothesize that racial-ethnic, gender, and SES inequalities in health will decrease between middle and late life.

DATA AND METHODS

Data from Waves 1 through 10 of the Health and Retirement Study (HRS) were used to test the study hypotheses. The HRS uses a multistage area probability sample, with a target population of all community-dwelling English- or Spanish-speaking adults in the contiguous United States over age 50 (spouses of respondents were interviewed regardless of age eligibility). Blacks and Hispanics were oversampled to allow independent analysis of racial groups. Respondents were interviewed biennially between 1992 and 2010 (response rates were 82% to 89%). The first interviews for the 1931-to-1941, 1942-to-1947, and 1948-to-1953 birth cohorts were in 1992, 1998, and 2004, respectively. A minor proportion of individuals at the target ages of this study were institutionalized, and any respondents who were institutionalized after their baseline interview remained in the study. Nonetheless, health disparities may be somewhat understated given the exclusion of institutionalized populations at baseline. Given the relatively small numbers of non-Mexican Hispanics/Latinos in the HRS, and the fact that Hispanic subgroups have different health profiles ( Markides et al. 2007), Mexican Americans were the only Hispanics included in this study. In addition, we excluded racial groups other than blacks and whites due to small sample sizes. Thus, the final analytic sample included 12,976 respondents (4,967 white men, 4,832 white women, 997 black men, 1,348 black women, 426 Mexican American men, and 406 Mexican American women).

Dependent Variable

Self-rated health was determined by respondents' answers to the question, "In general, would you say your health is: excellent, very good, good, fair, or poor?" at each wave of the survey; responses ranged from 1 ("poor") to 5 ("excellent"). Self-rated health has been shown to be a reliable and valid measure of general health status and to be predictive of morbidity ( Ferraro, Farmer, and Wybraniec 1997) and mortality ( Idler and Benyamini 1997). It remains a strong predictor of mortality even after accounting for known demographic, social, and medical risk factors, and it is more predictive of mortality than physician assessments ( Idler and Benyamini 1997). In addition, studies have found that self-rated health has predictive validity for more objective health measures and mortality across racial-ethnic and gender subgroups ( Johnson and Wolinsky, 1994; Kimbro, Gorman, and Schachter 2012).

Independent Variables

Social, Demographic, and Economic Characteristics

Binary variables indexed self-reported race-ethnicity: non-Hispanic black (yes = 1) and Mexican American (yes = 1), with non-Hispanic whites serving as the reference group. Gender was also measured by a binary variable (women = 1, men = 0). Age was measured in years. Socioeconomic measures included years of education and household income (logged sum of all household wages and salaries); income equivalencies across households were created by dividing income by the square root of the number of household members ( Brady 2009).

Control Variables

To account for different rates of dropout and death attrition, a measure of the proportion of waves missing and a binary indicator of whether the respondent died (yes = 1) during the observation were included in the models. Similar approaches have been shown to be an efficient and effective method for minimizing biases associated with sample attrition ( Brown et al. 2012). All models included a binary indicator of whether the respondent was born outside of the United States (yes = 1) given the well-documented immigrant health advantage ( Markides et al. 2007). In order to account for cohort effects and avoid confounding age-related changes with cohort differences, analyses controlled for birth cohorts ( Brown et al. 2012; Yang and Lee 2009). Members of the HRS birth cohort, who serve as the reference group, were born between 1931 and 1941. Other cohorts were born from 1942 to 1947 (war baby cohort) and 1948 to 1953 (early baby boomer cohort). These cohorts correspond to secular changes and shared historical experiences as well as the study cohorts.

Analysis

This study tests the research hypotheses in two steps. First, to examine how race-ethnicity, gender, and SES combine to shape the mean levels of health across ages 51 to 77, we used data from Waves 1 through 10 and multilevel models that were estimated within a mixed-model framework. A hierarchical strategy was used, where repeated observations (Level 1) are nested respondents (Level 2). These models are well suited for panel data because they adjust for non-independence of observations and correlations within clusters ( Raudenbush and Byrk 2002). In addition to the fixed effects of covariates, random effects (subject-specific deviations) for the intercept are included in the model. To test the alternative multiple-hierarchy stratification hypotheses, we examined the main effects of, and interactions among, race-ethnicity, gender, and SES on health outcomes. Due to the complexity of interpreting coefficients for interactions among three or more variables, models including Race-ethnicity × SES interactions are stratified by gender, and we use Chow tests to determine whether the coefficients for race-ethnicity and SES (and their interactions) statistically differ for men and women. This approach is commonly used for testing intersectionality hypotheses and provides similar-yet more comprehensible-information than three-way interactions ( Landry 2007).

While both multiple jeopardy and intersectionality hypotheses predict that race-ethnicity, gender, and SES affect health, and that women of color with low SES will have the worst health of all race-ethnicity/gender/SES subgroups, they differ in their expectations regarding how these social factors combine to shape health. Evidence that there are nonsignificant interactions among race-ethnicity, gender, and SES-suggesting that the health consequences of racial-ethnic, gender, and SES inequality are independent of each other-would constitute support for the multiple jeopardy hypothesis ( Beal 1970; Greenman and Xie 2008). On the other hand, support for the intersectionality hypothesis would be indicated by statistically significant interactions among race-ethnicity, gender, and SES ( Brown and Hargrove 2013; Hinze et al. 2012; Umberson et al. 2014). This would suggest that their effects on health are conditional upon each other. For example, findings that racial-ethnic inequality in health is greater among women than among men (as indicated by a Chow test), or that the health benefits of higher SES are weaker (or nonexistent) among people of color (especially women of color) than among whites, would be consistent with the inter-sectionality hypothesis.

The second stage of the analysis extends the first set of analyses by estimating age interactions with race-ethnicity, gender, and SES to determine whether the joint consequences of these social factors are conditional upon age. Random coefficient growth curve models are used to test the aging-as-leveler, persistent inequality, and cumulative disadvantage hypotheses. Growth curve models generated individual trajectories that were based on estimates of person-specific intercepts or initial values (at age 51) and age slopes that described intra-individual patterns of change in health as a function of age between ages 51 and 77. We used linear growth curves with random intercepts and random linear slopes to estimate age trajectories of self-rated health because comparisons of nested likelihood ratio tests of various shapes of health trajectories (e.g., linear, quadratic, or cubic models) suggested that these provided the best fit to the data. Regressing coefficients for gender and race-ethnicity, and their interactions, on the age slopes provided information about the pattern of the health inequality over time and formal tests of the life course hypotheses. Findings that age slopes are similar across groups would be consistent with the persistent inequality hypothesis, while findings that health inequalities narrow or widen with age would be consistent with aging-as-leveler or cumulative disadvantage hypothesis, respectively ( Haas and Rohlfsen 2010; Willson et al. 2007).

In both stages of analyses, initial models examining additive effects of key covariates were followed by models investigating interactions among these factors. To reduce the likelihood of reverse causality, household income, a time-varying measure, was lagged by one wave. Given subgroup differences in missing waves and mortality rates (see for information on group differences in attrition), and the selective nature of attrition, conventional methods that exclude respondents with incomplete data yield biased estimates of inequality in health. To avoid such biases, this study used hierarchical linear models in tandem with maximum likelihood estimation. This approach allowed us to incorporate respondents who have been observed only once, including those who attrited, during the observation period. To account for racial-ethnic/gender differences in dropout and death attrition, indicators of proportion of missing interviews and death were included in the models. Item nonresponse was addressed by using listwise deletion, resulting in a loss of 29 respondents. All analyses were performed using SAS 9.3 and weighted using survey weights provided by the HRS.

Table 1

RESULTS

Results in show that self-rated health, SES, and control variables differ across racial-ethnic and gender lines. Blacks and Mexican Americans tend to be disadvantaged relative to their white counterparts in terms of health and SES. As expected, results reveal distinct disadvantages for women of color, who have the worst self-rated health and the lowest income levels. Taken together, the patterns of differences in self-rated health and social determinants of health show that those in more privileged positions-namely, whites, men, and white men in particular-tend to be more advantaged in mid- to later life, while black and Mexican American women appear to be the most disadvantaged racial-ethnic/gender groups. These results are largely consistent with well-established inequalities ( Brown and Hargrove 2013).

Are the Joint Health Consequences of Racial-ethnic, Gender, and SES Inequality Additive or Multiplicative?

presents multilevel models of self-rated health using data from Waves 1 through 10 of the HRS. These models provide statistical tests of our multiple-hierarchy stratification hypotheses regarding racial differences in self-rated health and the (potentially) gendered and classed nature of these differences. All models control for birth cohort, nativity, and dropout and death attrition. Results from Model 1 imply that self-rated health varies along both racial-ethnic and gender lines, as there are statistically significant, negative coefficients for black and Mexican American men and women, as well as the significant Chow tests indicating that women have worse self-rated health than men. Thus, racial-ethnic minorities and women, and especially those who fall in both of those categories, have worse self-rated health than white males. Moreover, the significant Chow tests for the black and Mexican American coefficients suggest that the effects of racial-ethnic and gender inequalities are multiplicative, indicating that the magnitude of the racial-ethnic inequality in self-rated health is greater among women than men, consistent with the intersectionality hypothesis. Results show that all racial-ethnic/gender groups experience poorer self-rated health than white men, and that racial-ethnic minority women have the worst self-rated health. Specifically, white men have the highest self-rated health, followed in descending order by white women, black and Mexican American men, and black and Mexican American women, respectively. Supplemental analyses indicated that there were no statistically significant differences between blacks and Mexican Americans of the same gender (results not shown).

Table 2

Model 2 of adds SES measures. Results in Model 2 show that both education and income are positively associated with self-rated health. Moreover, adjusting for these SES indicators reduces the magnitude of racial-ethnic inequality in self-rated health, though they remain significant.

Model 3 of tests for interactions among race-ethnicity, gender, and SES. Statistically significant, positive coefficients for education in tandem with significant negative coefficients for interactions between black/Mexican American and education suggest that the education-health gradient is less steep for blacks and Mexican Americans than whites (intersectionality hypothesis); this is the case for both men and women. The association between income and self-rated health is similar across race-ethnicity among men. However, findings indicate that higher levels of income are less beneficial in terms of health for black and Mexican American women than white women. Furthermore, the statistically significant Chow test for gender differences in the Black × Income coefficient indicates that there is a three-way interaction among race-ethnicity, gender, and SES-providing further evidence to support the intersectionality hypothesis. These findings are presented in and , which provide graphical illustrations of the joint consequences of race-ethnicity, gender, and SES on self-rated health (based on estimates from Model 3 of ). Results suggest that white men with high levels of education have the best health and that black and Mexican American women with low levels of education have the worst health, with other race-ethnicity/gender/education groups occupying liminal statuses. Also apparent is the fact that racial-ethnic gaps in health are larger at higher levels of education than at lower levels of education for both men and women. Similarly, among women, racial-ethnic inequality in health is greater at higher levels of income than at lower levels of income. Interestingly, Mexican American women with low incomes have better health than their white counterparts, which may be due to the buffering effects of sociocultural factors.

Results indicate that sociodemographic control variables are predictive of self-rated health. Specifically, compared to the HRS cohort, the war baby and early baby boomer cohorts report worse health, as do respondents with higher proportions of missing waves and those who died compared to respondents who did not. The effects of these sociodemographic control variables are generally similar across gender, though the magnitude of the effect of proportion of waves missing is greater among men than among women.

Does Inequality in Health Decrease, Remain Stable, or Increase Between Middle and Late Life?

presents growth curve models of self-rated health between ages 51 and 77. These models provide statistical tests of our life course hypotheses (aging as leveler, persistent inequality, and cumulative disadvantage) regarding racial-ethnic differences in self-rated health intercepts (levels at age 51) and slopes (rates of change with age) and the potentially gendered and classed nature of these differences. Each of these models controls for birth cohort, nativity, and dropout and death attrition. Model 1 of tests for racial-ethnic and gender inequality in self-rated health trajectories. As predicted by the aging-as-leveler hypothesis, the statistically significant negative coefficients for self-rated health intercepts among black men and women (−.487 and −.691, respectively) in tandem with the significant positive black coefficients for self-rated health age-slopes among men and women (.014 and .015, respectively) indicate that health gaps between black men and women and their same-gender white counterparts decrease between ages 51 and 77 by 72% (from .487 to .135) and 56% (from .691 to .301), respectively. Similarly, the statistically significant coefficients for Mexican American men's self-rated health intercepts (−.682) and slopes (.016) reveal that Mexican American-white health inequality among men declines between ages 51 and 77 by 60% (from .682 to .275)-consistent with the aging-as-leveler hypothesis. Overall, results from Model 1 show that black men and women and Mexican American men experience declines in self-rated health with age at approximately half the rate of their white counterparts. Mexican American women, on the other hand, experience persistent inequality in self-rated health relative to white women. These findings (based on Model 1 of ) are illustrated in .

Table 3

Model 2 of adds SES measures. For both men and women, education and income are positively associated with self-rated health intercepts but negatively associated with self-rated health slopes. This suggests that, compared to individuals with lower levels of education and income, those with higher levels of education and income have better self-rated health at age 51 but steeper health declines between ages 51 and 77, providing further evidence to support the aging-as-leveler hypothesis. It is also important to note that adjusting for SES measures reduced the magnitude of racial-ethnic inequality in health intercepts, though they remain statistically significant. Overall, results from Chow tests in Model 1 indicate that there are gender differences in the influences of race and education on self-rated health intercepts but that racial-ethnic and SES inequalities in self-rated health age slopes are not gendered.

Model 3 of adds tests for interactions between race-ethnicity, gender, and SES on self-rated health trajectories. The statistically significant negative slope coefficient for the Black × Education term (−.002) among men suggests that black men with higher levels of education experience more rapid declines in self-rated health with age compared to white men with similar levels of education as well as compared to black men with lower levels of education. Thus, the self-rated health gap between black and white men with higher levels of education increases with age, consistent with the cumulative disadvantage hypothesis; conversely, the magnitude of the self-rated health disparity between black men with higher levels of education versus those with lower levels of education declines with age, as predicted by the aging-as-leveler hypothesis. Similarly, the significant negative slope coefficient for the Black × Education term among women (−.002) suggests that black women with higher levels of education also experience particularly steep self-rated health declines with age. In fact, between ages 51 and 77, black women with 16 years of education experience self-rated health declines that are 438% steeper than black women with eight years of education (.700 vs. .130). and present graphical illustrations of the estimates from Model 3 of for men and women, respectively. For the sake of concision, Mexican Americans are not included in and ; results showed that there are no significant Mexican American × Education interactions on the slopes of self-rated health. There is also no evidence of significant interactions between race-ethnicity and income on age slopes of self-rated health among either men or women.

Several of the sociodemographic control variables affect trajectories of self-rated health. Results generally indicate that later birth cohorts, higher proportions of missing waves, and death were predictive of worse self-rated health intercepts; and being foreign-born, higher proportions of missing waves, and death are also predictive of more rapid declines in self-rated health with advancing age (though the effects of these factors varied somewhat by gender). Importantly, our substantive findings on racial-ethnic, gender, and SES differences in self-rated health trajectories are similar regardless of whether cohort and attrition measures are included in the models, suggesting that our results are robust to different cohort and attrition specifications.

DISCUSSION

Although the social stratification of health along racial-ethnic, gender, and socioeconomic lines is well established in the literature, fewer quantitative studies have examined how racial-ethnic, gender, and socioeconomic inequality combine to affect health. Rather, studies have tended to examine their separate influences or assume that that their health consequences are additive. Moreover, the few studies that have examined the joint health effects of multiple social statuses have not incorporated life course perspectives and methods, leaving unanswered the question of how the simultaneous and intersecting consequences of race-ethnicity, gender, SES, and age influence health inequality. Given current and projected demographic trends related to the aging and diversification of the U.S. population, this unanswered question represents a critical gap in the extant literature on the social stratification of health. Our study addresses this gap and extends previous work in several important ways.

First, aside from a few recent studies, this study is among the first quantitative studies to employ a multiple-hierarchy stratification approach to conceptualizing and measuring racial-ethnic and gender inequality in health. For example, we extended previous research that compared the health of various racial-ethnic/gender subgroups to others (e.g., Geronimus et al. 2007; Read and Gorman 2006; Warner and Brown 2011) by explicitly testing alternative multiple-hierarchy stratification hypotheses regarding whether social statuses combined in an additive (multiple jeopardy) or multiplicative (intersectionality) fashion. Our findings revealed that racial-ethnic inequality in self-rated health is gendered: racial-ethnic inequality was greater among women than among men, and the female health disadvantage tended to be greater among blacks and Mexican Americans than among whites. Black and Mexican American women had the worst self-rated health, while white men had the best. Importantly, results strongly supported the intersectionality hypothesis, as indicated by the interactive relationships between race-ethnicity and gender. Specifically, we found that being black or Mexican American and female has deleterious health consequences beyond those already accounted for by race-ethnicity or gender alone.

Second, this study extends extant research by investigating how SES intersects with race-ethnicity and gender to affect the social distribution of self-rated health. Few studies have examined how SES interacts with race-ethnicity and gender to shape health (e.g., Cummings and Jackson 2008; Hayward et al. 2000; Hinze et al. 2012). Of these, only two focused on the social distribution of self-rated health (e.g., Cummings and Jackson 2008; Hinze et al. 2012). Results from our study indicated that there are consistent interactions between SES and race-ethnicity and, to a lesser extent, among SES, race-ethnicity, and gender. Specifically, findings showed that while education is positively associated with self-rated health, the education-health gradient is steeper for white men and women than for their black and Mexican American counterparts. While there were no racial-ethnic differences in the income-health relationship among men, the income-health gradient was steeper for white women than for black and Mexican American women. Moreover, there was a statistically significant three-way interaction among race-ethnicity, gender, and income, indicating that black women receive diminished self-rated health returns to income, compared to white women and men as well as black men. Socioeconomic resources do not appear to confer self-rated health advantages equally across racial-ethnic and gender groups. Within the context of unequal opportunities, attempts by marginalized groups to acquire traditional socioeconomic resources in the face of blocked opportunities may produce negative health consequences ( Jackson and Williams 2006; Pearson 2008). Taken together, these results reveal that racial-ethnic, gender, and SES stratification combine in mostly multiplicative ways, consistent with the intersectionality hypothesis. Importantly, these interconnections would not have been evident if we had employed a more conventional approach, such as examining the health consequences of race-ethnicity, gender, and SES separately or assuming that their effects were additive. This suggests that interactive, multiple-hierarchy stratification approaches have considerable utility for uncovering a more nuanced and complex understanding of between- and within-group health inequalities than unidimensional and additive approaches.

A third key contribution of this study is that we integrated life course perspectives with a multiple-hierarchy stratification approach in order to understand intracohort inequality dynamics. Specifically, we extended previous studies (e.g., Brown and Hargrove 2013; Hinze et al. 2012) by examining whether the simultaneous and overlapping effects of age, race-ethnicity, gender, and SES result in narrowing, persistent, or widening self-rated health gaps between middle and later life. Scholarship on the social stratification of aging and health illustrates that the health consequences of social positions are likely to vary with age ( House et al. 2005; Willson et al. 2007). The relative inattention to how age intersects with race-ethnicity, gender, and SES to shape health trajectories, and the related overreliance on cross-sectional data, in research on the social stratification of health reflects an implicit assumption that health inequality is stable across the life course. While a handful of studies have examined differences in age trajectories of health along a single social status, such as race-ethnicity ( Haas and Rohlfsen 2010; Kim and Miech 2009), gender ( Anderson et al. 1998; Liang et al. 2008), or SES ( Lantz et al. 2001; Ross and Wu 1996), we are not aware of any other studies that have tested how age simultaneously interacts with all three social statuses to shape health.

Findings from our initial growth curve models, which examined age differences along various social dimensions separately and in an additive fashion, suggested that there was strong support for the aging-as-leveler hypothesis. For example, racial-ethnic gaps in self-rated health largely appeared to decline between middle and late life, consistent with previous studies ( Brown et al. 2012). Furthermore, the health advantages associated with higher levels of education and income appeared to erode as individuals aged, similar to findings from previous research ( House et al. 2005; Willson et al. 2007). However, our subsequent growth curve models, which examined the simultaneous intersections of age, race-ethnicity, gender, and SES, provided evidence of three-way interactions between age, race-ethnicity, and SES. These results, therefore, revealed more nuanced and heterogeneous social patterning of health trajectories than was evident from the simpler models. Specifically, black men and women with higher levels of education exhibit more rapid health declines with age compared to white men and women with comparable levels of education. Consequently, the self-rated health gap between highly educated blacks and whites increases with age, as predicted by the cumulative disadvantage hypothesis. Moreover, compared to whites, blacks experience more rapid erosion of the health benefits of higher levels of education with age. This is consistent with emerging evidence suggesting that blacks experience diminished health returns to SES relative to whites, which may be due to the stress of achieving higher SES in the face of opportunities that have been blocked by institutional racism and in the midst of increased encounters of interpersonal racism ( Colen 2011; Farmer and Ferraro 2005; Jackson and Williams 2006; Pearson 2008). Others have also noted racial-ethnic differences in the economic returns on education ( Oliver and Shapiro 2006) as well as the presence of residential segregation even at higher levels of SES ( Pattillo 2005), both of which could contribute to vast differences across racial-ethnic groups in living conditions that matter for health among the highly educated.

Importantly, our findings of three-way interactions between age, race-ethnicity, and SES were not evident in our initial, simpler models. These results, which are net of differential prospective attrition, underscore the fact that age is an additional dimension of inequality that simultaneously intersects with other dimensions. In light of our findings that the extent of racial-ethnic, gender, and SES stratification in health varies with age, it is clear that the common implicit assumption that health disparities are stable across the life course obscures the dynamic and layered nature of health disparities between middle and late life. Our results suggest that life course approaches that contextualize health experiences along racial-ethnic, gender, and SES lines have considerable utility for understanding the social stratification of health trajectories.

While we used existing racial-ethnic and gender categories to understand patterns of health inequality, we recognize that these categories are socially constructed and historically contingent. Moreover, consistent with critical race and feminist perspectives, we interpret racial-ethnic and gender inequalities not as effects of race-ethnicity and gender per se but, rather, as a result of relations of domination and subordination stemming from racism, sexism, and their consequences for class inequality ( McCall 2005; Weber 2010; Zuberi and Bonilla-Silva 2008). For example, racial residential segregation, a manifestation of institutional racism that remains stubbornly high, leads to differential exposure to neighborhood risks and resources that affect health ( Williams and Collins 2001). Indeed, some studies have found that accounting for racial-ethnic differences in neighborhood conditions reduces the minority health disadvantage ( LaVeist, Pollack, et al. 2011). In addition, there is accumulating evidence that racial-ethnic health inequality stems, in part, from unequal exposure to chronic and discrimination stressors ( Bratter and Gorman 2011; Goosby and Heidbrink 2013; Thoits 2010; Williams and Mohammed 2009). The effects may be particularly harmful for nonwhite women, who may experience especially high levels of discrimination stress given that they are exposed to chronic stress-ors associated with sexism and racism ( Grollman 2014), which simultaneously contribute to their lower class status ( Davis 1981), as well as gendered racial discrimination, which is simultaneously based on both their race-ethnicity and gender ( Harnois and Ifatunji 2011). The data used for this study do not include information on the neighborhood conditions of the respondents or various types of discrimination they may have faced. However, future research should examine the roles of these factors in order to better understand the mechanisms through which racial-ethnic inequality in health becomes gendered.

Although this study highlights the unique disadvantages that racial-ethnic minority women endure, racial-ethnic minority men also experience distinct racialized limits on their life chances-for example, disproportionately high rates of unemployment, incarceration, victimization due to violent crimes, and mortality ( Griffith, Ellis, and Allen 2013; Pettit 2012; Williams 2003). Thus, it is not fruitful to view the examination of gendered racial inequality as a within-race competition of which gender is the most disadvantaged. Rather, consistent with the life course principle of linked lives ( Elder, Johnson and Crosnoe 2003), we acknowledge that the distinct disadvantages experienced by racial-ethnic minority men and women have reciprocal effects within families and communities. In our patriarchal society, women are more likely to be family caretakers and, thus, are especially likely to experience vicarious distress and vicarious racism ( Nuru-Jeter et al. 2008) when family members experience stressful events and health problems ( Turner and Avison 2003). Consequently, in addition to the gendered and racialized burdens that racial-ethnic minority women experience, the ways in which society marginalizes and situates men of color-who are their family members, partners, and friends-likely affects and limits the life chances of women of color, through decreased socioeconomic resources, compromised family functioning, and increased levels of stress and isolation ( Lee and Wildeman 2013). Therefore, future research should examine the relative contributions of disadvantages and stressors experienced by black and Mexican American women as well as their significant others to their especially poor health.

This study has several limitations worth noting. First, small sample sizes in the HRS preclude inclusion of Asians, Native Americans, and Hispanics other than those of Mexican descent in health disparities research. Furthermore, although blacks and Hispanics were oversampled, there are relatively modest sample sizes of blacks and Hispanics with high SES in the HRS, which may limit the statistical power necessary to detect racial-ethnic differences in the effects of high SES on health. Additional data collection efforts are needed to investigate gender and SES heterogeneity with respect to health among racial-ethnic minorities. A second limitation of this study is our inability to examine the intersections of a wider array of social factors. Multiple-hierarchy stratification and intersectionality perspectives in particular emphasize the salience of multiple identities and social statues (beyond just race-ethnicity, gender, SES, and age), including nativity, sexual orientation, disability, and religion ( Schulz and Mullings 2006). Though it is beyond the scope of this study, future research should examine how a wider array of social factors simultaneously and interactively affect health trajectories. While there are a number of analytic challenges to accomplishing this goal solely through quantitative research, qualitative approaches are particularly well suited for providing rich theoretical insights and giving voice to multiply disadvantaged groups ( McCall 2005).

Third, although the analyses account for right-censoring by controlling for prospective death and dropout attrition, results presented here may be biased given well-documented gender and racial-ethnic differences in mortality rates prior to midlife. Specifically, excess mortality among men and racial-ethnic minorities, relative to women and whites, may lead to overestimates of gender disparities and conservative estimates of racial-ethnic inequality in health ( Hayward et al. 2000). Accordingly, findings should be interpreted as conditional on survival to midlife. Fourth, the health measure used in this study-self-rated health-is subjective, and when respondents rate their own health, it is unclear to whom they are comparing themselves. Nonetheless, previous research has validated self-rated health as a useful measure of general health and a predictor of more objective indicators of health, such as clinical assessments, doctor-diagnosed health conditions, and mortality ( Ferraro et al. 1997; Idler and Benyamini 1997) across racial-ethnic, gender, and age groups ( Johnson and Wolinsky 1994; Kimbro et al. 2012). Future research is needed to determine whether and how race-ethnicity, gender, SES, and age intersect to affect other measures of morbidity.

Despite these limitations, we find strong support for our hypothesis that racial-ethnic health inequality varies by gender and SES. These findings are significant because they highlight the fact that health is shaped by multiple dimensions of inequality simultaneously and interactively. Moreover, by focusing on heterogeneity within racial-ethnic groups, our findings render visible the experiences of women of color with low SES, who are especially vulnerable in terms of health due to their disadvantaged positions within racial-ethnic, gender, and SES hierarchies. The health disparities highlighted in this study, therefore, reflect an excess and unequal distribution of human suffering that requires more than the typical single-issue policy responses. Policies that address the multiple pathways to health inequality, while considering the differential effects of those policies on racial-ethnic, gender, and socioeconomic inequality more broadly, are needed.

Acknowledgments

FUNDING

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Institute on Aging (RCMAR Grant P30AG043073) and the Robert Wood Johnson Foundation (Grant No. 64300 and Subaward No. 100927DLH216-02). are MAR, because all of the data are used in the analysis and a fully efficient estimation procedure (maximum likelihood) is utilized, estimates from the growth curve models are asymptotically unbiased (Raudenbush and Bryk 2002).

Biographies

Tyson H. Brown is an assistant professor in the Department of Sociology at Duke University. His research focuses on understanding how and why race-ethnicity and other social factors, such as gender, class, immigration, and age, intersect to shape health across the life course. He is also interested in identifying the structural and psychosocial mechanisms underlying between- and within-group health inequalities.

Liana J. Richardson is an assistant professor in the Department of Sociology at the University of North Carolina at Chapel Hill. She is also a fellow at the Carolina Population Center. Her research focuses on the link between social inequalities and health, with particular interest in examining health disparities as both causes and consequences of social inequalities within and across generations.

Taylor W. Hargrove is a postdoctoral fellow in the Department of Sociology and Carolina Population Center at the University of North Carolina at Chapel Hill. Broadly, her research interests include social stratification, race-ethnicity, medical sociology, and aging and the life course. She is currently engaged in research that investigates the ways in which race, gender, and social class combine to influence health disparities. She is also interested in examining factors that lead to within-group heterogeneity in health among black Americans, such as stress, discrimination, and skin tone.

Courtney S. Thomas is a postdoctoral fellow at the University of California at Los Angeles and an assistant professor of sociology and African American and Africana studies at the University of Kentucky. Her research focuses on the social and psychological factors that contribute to health inequality at the intersections of race, gender, and class. She is particularly interested in identifying the mechanisms underlying health paradoxes such as poor outcomes among middle-class blacks.

Footnotes

¹ We refer to blacks and Mexican Americans collectively when asserting that these racial and ethnic minorities may exhibit similar health consequences of social factors, such as racism. However, we recognize that blacks and Mexican Americans have faced distinct sociohistorical events and processes that have differentially shaped life chances among, social standing of, and society's relations to each group. Our use of a collective term to refer to these groups, therefore, should not be interpreted as suggesting that blacks and Mexican Americans have equivalent lived experiences and reactions to such experiences. Instead, we are merely acknowledging the possibility of shared social experiences across these groups (as a result of their positioning within the racial and socioeconomic hierarchies) that can produce parallel health outcomes among them.

²Despite disproportionately high rates of incarceration among racial- and ethnic-minority men ( Pettit 2012), less than 1% of black and Hispanic men age 55 or older in 2000 were incarcerated ( Beck and Karberg 2001), suggesting that differential rates of incarceration are unlikely to significantly bias estimates of racial-ethnic and gender inequality in health among middle-aged and older adults.

³ The distributions of both education and income differ by race-ethnicity, with relatively smaller proportions of blacks and Hispanics having high levels of socioeconomic status compared to whites. However, sensitivity analyses that used windsorized measures of education and income yielded substantively similar results to those presented in this study, suggesting that our findings are robust to different education and income distributions.

⁴Consistent with previous studies on health trajectories that have used growth curve models that assume a Gaussian distribution ( Kim and Miech 2009; Yang and Lee 2009), this study uses the PROC MIXED procedure in SAS 9.3 to specify a multilevel model. Although the outcome variable does not conform to the Gaussian distribution, ancillary analysis using the Shapiro-Francia test of normality indicates that the residuals are distributed normally and thus the normality assumption is not violated. Moreover, supplemental mixed models using log- and square root-transformed health outcomes, as well as ordinal fixed-effects models, produced comparable results to those assuming a normal distribution, suggesting that the results are robust to alternative assumptions regarding the distribution (see Warner and Brown 2011). Therefore, the final models presented here were estimated using the computationally efficient PROC MIXED procedure.

⁵Under these circumstances, Raudenbush and Bryk (2002) note that (1) the data may be assumed to be missing at random (MAR), meaning that the probability of missing a time point is independent of missing data given the observed data, and (2) this is a reasonable assumption when the observed data include variables related to both missingness and the dependent variable. Assuming the data

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Using Multiple-hierarchy Stratification and Life Course Approaches to Understand Health Inequalities: The Intersecting Consequences of Race, Gender, SES, and Age

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This study examines how the intersecting consequences of race-ethnicity, gender, socioeconomics status (SES), and age influence health inequality. We draw on multiple-hierarchy stratification and life course perspectives to address two main research questions. First, does racial-ethnic stratification of health vary by gender and/or SES?

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An article which uses thematic analysis

Children's understandings’ of obesity, a thematic analysis

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Abstract

Childhood obesity is a major concern in today's society. Research suggests the inclusion of the views and understandings of a target group facilitates strategies that have better efficacy. The objective of this study was to explore the concepts and themes that make up children's understandings of the causes and consequences of obesity. Participants were selected from Reception (4-5 years old) and Year 6 (10-11 years old), and attended a school in an area of Sunderland, in North East England. Participants were separated according to age and gender, resulting in four focus groups, run across two sessions. A thematic analysis (Braun & Clarke, 2006) identified overarching themes evident across all groups, suggesting the key concepts that contribute to children's understandings of obesity are "Knowledge through Education," "Role Models," "Fat is Bad," and "Mixed Messages." The implications of these findings and considerations of the methodology are discussed in full.

Keywords: Obesity/overweight, health behaviour, children research, qualitative research

The Health Survey for England 2009 illustrated that 65.9% of men and 56.9% of women have a body mass index (BMI) higher than 25 kg/m ², classing them as overweight, obese (>30 kg/m ²), or morbidly obese (>40 kg/m ²). Obesity is linked to many chronic illnesses, including type II diabetes, heart disease, and some cancers-specifically bowel and others within the digestive system (Renehan, Tyson, Egger, Heller, & Zwahlen, 2008). As a result, the direct cost to the National Health Service (NHS) of treating obesity was estimated to be between £991 and £1,124 million, for the 2001/2002 financial year (McCormick & Stone, 2007).

Childhood obesity is of particular concern because obese children are far more likely than children of a normal weight to become obese adults (Alexander & Sherman, 1991). The Health Survey for England 2009 showed that between 1995 and 2008, the percentage of overweight and obese girls rose from 25.5 to 29.2% and from 24.5 to 31.4% for boys. This is despite the fact that during the same period reported total energy intake in the United Kingdom (UK) fell by around 20% (Statistics on Obesity, Physical Activity and Diet England, 2006). These contradictory figures highlight the complexity of factors contributing to obesity, pointing to issues such as levels of physical activity, which have significantly fallen over the past two decades (Prentice & Jebb, 1995).

Many other factors influence incidences of obesity. The negative impact of childhood obesity causes the greatest concern and needs to be further understood. Obese children are more likely to become obese adults and experience increased health problems. Knowler, Pettitt, and Saad ( 1991), highlighted the links between childhood obesity and a poor immune system, risk of raised blood pressure, and cardiovascular problems. Studies have also identified that overweight and obese children are more likely to suffer psychological problems associated with low self-esteem, bullying, and social exclusion (Breat, Mervielde, & Vandereycken, 1997).

On an international scale, obesity can be seen as a problem of the developed world, a result of economic wealth, high food availability, and low levels of manual labour leading to lower levels of physical activity. This is in conjunction with high levels of car ownership and wide ranging public transport systems adding to the problem. In short, at the heart of obesity lies a homeostatic biological system that works constantly to maintain energy balance to keep the body at a constant weight. This system has not yet adapted to the world in which we currently live because the pace of technological progress has surpassed evolution resulting in a more sedentary lifestyle (Department of Innovation Universities and Skills, 2007). One surprising feature of the geographical distribution of obesity is its increased prevalence in economically and socially deprived areas in the western world, including the focus of this current piece of research, the United Kingdom. This phenomenon is very much a recent development, because historically deprived areas tended to see higher levels of under-nutrition. Brunt, Lester, Davies, and Williams ( 2008) illustrate how this situation has now reversed. They found between 1995 and 2005 the gap between obesity levels in the most deprived areas compared to the least (the latter typically having the higher levels) was steadily closing, and that by 2005 obesity levels in the most deprived areas had overtaken those in the least deprived areas, a phenomena that persists today.

The Childhood Measurement Programme (Department of Health and Department for Children, Schools and Families, 2008) demonstrated Sunderland in the north-east of England has some of the highest levels of overweight and obese children in the United Kingdom. This same publication also points out the strong positive correlation between areas considered as deprived and levels of obesity in children in Reception (4-5 year olds) and Year 6 (10-11 year olds). Areas of Sunderland are considered to be economically and socially deprived meaning the children who live there can be considered high risk. The statistics relating to Sunderland, where this study took place, demonstrate that 27.8% of Reception-aged children are either overweight or obese and for Year 6 pupils this rises to 38.4%.

The Foresight Report (Department of Innovation Universities and Skills, 2007), tackling obesity, points out that current policies are failing because they do not provide the depth and range of interventions needed. This might lead to positive interventions being ineffective if they are undermined by other areas in society such as social factors and the power of media advertising. The government launched its Healthy Schools Initiative in 2005; however, there has been no substantial reduction in obesity levels since 2005 (Department of Health and Department for Children, Schools and Families, 2008). With this in mind it would seem timely to approach the problem from a different perspective. Effective policies to tackle obesity need to consider all parties involved. However, current policies have been formed using a top down approach i.e., from government, health and education professionals, and even celebrity chefs! Even though these groups are likely to have a broad understanding of the problem from its roots to the long-term consequences, there has been a notable failure to take into consideration the understandings of the individuals at highest risk of obesity, the children themselves. There is growing evidence that interventions incorporating the views of the target population have a greater level of success (Hesketh, Water, Green, Salmon, & Williams, 2005). In the United Kingdom there has been a strong movement to ensure the inclusion of children in decision making particularly in relation to issues that directly affect them such as education, social care, and health (Department of Health, 2002; Department of Health and Department for Education and Skills, 2004). The collection and dissemination of the understandings of children relating to obesity could provide an insight into why so many strategies are failing. This in turn could lead to the development of policies that can be delivered to provide more successful outcomes.

There is a clear shortage of research examining children's understandings' of obesity, the studies that have attempted to explore this domain have focused on exploring parent and care giver perceptions (Young-Hyman, Herman, Scott, & Schlundt, 1999), and the understandings of health professionals (Chamberlin, Sherman, Jain, Powers, & Whitaker, 2002). More recently studies have considered the understandings of care givers, health professionals, and teachers alongside those of the children themselves (Borra, Kelly, Shirreffs, Neville, & Geiger, 2003; Hesketh et al., 2005). Studies that have examined children's understanding have been focused on body image, overweight versus underweight (Hill & Silver, 1995), and peer perceptions of overweight and eating behaviour (Bell & Morgan, 2000; Oliver & Thelen, 1996), but not on the understandings' of the children themselves with regards to the causes and consequences of obesity.

Focus groups have proved to be a particularly useful method for collecting data from children, they are most effective with groups of three children and in situations where the children know and like each other. Groups must be carefully selected to ensure the children are comfortable with each other. Talking together in small groups is familiar territory for children because it simulates class work. This method allows the researcher to structure the discussion around themes or topics rather than direct questions. This in turn enables the children to take control of the discussion (Mauthner, 1997) with the researcher present to keep things on track. Conducting group discussions in single sex groups can also prove to be more successful because boys are often louder and more willing to talk and this can mean they direct the topic of conversation. It has also been noted the use of some sort of structured activity such as drawing, reading, or sorting cards, can help focus discussion in particular with young children. When discussing diet with children, nutritionists and dieticians regularly use replica food items to help visualise the topic under discussion and photos depicting scenes of physical activity have proved effective in qualitative studies (Hesketh et al., 2005).

In summary the objective of this research is to investigate the understandings of a high risk group of children (high risk because of their socio-economic status so determined by their locality), of some of the causes and consequences of obesity, and its links to diet and physical activity. The concepts and themes generated by this research should be used to provide an insight that may inform local policies and interventions that need to be developed to provide a broader and deeper range of options to address this multi-faceted issue.

Method

Approach

In order to address the gaps in current literature it was decided this research should focus on identifying themes within the participants understanding. This would provide the researcher with scope for further investigation of the subject in question. It was therefore decided that the most appropriate method of analysis would be a thematic analysis. However, there have been criticisms of this approach in the past due to the lack of clear guidelines for researchers employing such methods. This has subsequently contributed to some researchers omitting "how" they actually analysed their results (Attride-Stirling, 2001). It was of upmost importance to the authors in this current study to employ a clear, replicable, and transparent methodology.

Braun and Clarke ( 2006) outline a series of phases through which researchers must pass in order to produce a thematic analysis. This procedure allows a clear demarcation of thematic analysis, providing researchers with a well-defined explanation of what it is and how it is carried out whilst maintaining the "flexibility" tied to its epistemological position. The authors in this paper take a position that acknowledges our desire to incorporate the individual experiences of the participants and the meanings they attach to them. However, we also wish to consider the impact of the wider social context on these meanings. Braun and Clarke describe such a position as "contextualist," sitting firmly between essentialism or realism and constuctionism. Not all theorists describe these two poles of epistemological outlook in the same way; Madill et al. ( 2000) refers to them as "naive realist" and "radical relativist." Methodologies that go hand in hand with this mid-ground position are typically phenomenological in nature, but the flexibility of thematic analysis means that it can also be underpinned by an "in-between" epistemological position. Willig ( 2008, p. 13) summarises this by explaining a position that argues "while experience is always the product of interpretation and, therefore, constructed (and flexible) ... it is nevertheless 'real' to the person who is having the experience." We wish to consider the reality of obesity to the participants, through an exploration of their experiences and the meanings they attach to them, whilst incorporating the broader role society plays in contributing to and shaping the participants meaning making and subsequent understandings.

Participants

Twelve participants were selected through liaising with the school and class teachers, this was particularly important considering the sensitive nature of the research topic and the fact that the participants taking part in this study were children-a vulnerable group. Measures were taken to prevent any of the participants feeling stigmatised. Therefore, under the guidance of the class teachers, the participants approached to take part in the study were carefully selected to ensure no children who may have been made to feel uncomfortable by the discussion were included, and to make sure that the children selected to be in the same focus groups were comfortable with each other. Six (three boys and three girls) were selected from two school years; Reception, aged between 4 and 5 years and Year 6 aged between 10 and 11. The motivation for selecting these age groups was that government statistics relating to childhood obesity are published for these two age brackets. These age groups are viewed as critical points in measuring children's BMI and in monitoring their changing health status. Through looking at these age groups, it may help us to gain an insight into what understandings children arrive at school with (primarily shaped by their experiences set within a home environment) and those that they have later on in their school life when further social influence (school and peers) may play a role in shaping their understandings. Efforts were made to make the sample representative of ethnicities attending the school so a proportionate number of children of Bangladeshi and Afro-Caribbean heritage took part. Participants were not recruited on account of their BMI or weight status. The parents of the children were provided with a study information letter and, in addition, received a phone call from the school's community liaison officer to ensure that parents fully understood the nature of the study because the researcher was aware that for some parents English was not their first language. The phone calls were made in their mother tongue thus allowing the parents to sign the parental assent form with all their queries being answered. Participants were also asked for their verbal consent on the day prior to the study taking place.

Procedure

The study had received ethical approval from Northumbria University's School of Psychology and Sports Science Ethics Board prior to commencing. The researcher had also been approved by means of an enhanced criminal records background check clearing her to work with children; this approval was required by both the school and the university.

The focus groups all took place in the same quiet room at the school and were conducted by the principal investigator (referred to herein as the researcher). On arrival, the researcher introduced herself and provided name badges for the participants. The researcher briefly explained to the participants that she was there to talk to them about food and exercise. The researcher also explained to the participants that she wanted them to assume that she knew nothing, they were not being tested, and she was only interested in hearing what they had to say-not whether they were right or wrong. Verbal instructions were provided to the participants and they provided verbal assent prior to the recording commencing. A series of questions were developed by the research team, these were designed to keep the focus group sessions on track whilst exploring issues relevant to the research question. The sessions started initially with a discussion centred on the replica food items laid out on the table. Participants were asked to use the replica food and pick out healthy foods and make what they thought would be a healthy lunch. They were asked to explain why it was healthy and what made it healthy. Participants were then asked about foods they liked and why they liked them. In addition, they were asked about the sorts of things they normally ate at home and in school and things they liked to eat. Once conversation had dwindled concerning the replica food the researcher introduced the laminated picture cards, and the discussion moved to physical activity with the researcher encouraging the participants to explore the relationship between diet and exercise. Questions focussed on what activities they thought were healthy (as the images depicted activities that were both physical and sedentary; that is, one image of somebody running another of somebody playing computer games). The participants were asked about what sorts of activities they liked doing and what made those activities good for them. They were asked what activities they regularly engaged with, the sorts of sports their parents and siblings took part in, and the activities they did as families. The themes of discussion were encouraged around the two elements pertinent to any strategy looking to reduce obesity: healthy eating and physical activity. Furthermore, questions also probed at what the participants thought the benefits were of following a healthy lifestyle and what the consequences were of not following one. They were also asked what advice they would give somebody who wanted to be healthier and how important it was to them to be healthy. The focus group guide was intended to provide a structure but not rigidly dictate the line of questioning. The researcher included prompts and encouraged participants to expand on their initial responses and followed up on notions that the participants raised themselves. The sessions on the first day lasted between 20 and 30 min, ending when the participants input was insufficient to continue. At the end of each session the researcher read out the participant debrief and provided each participant with a parental debrief information sheet to take home.

In order to strengthen the analysis process and gather the most appropriate data, the researchers reviewed the recording made on the first day and reflected on the procedures employed in the focus groups. Similar approaches of reviewing data to informing further data collection are used in methods such as grounded theory and it was felt that doing so would strengthen the current study. The decision was made not to use the props (replica food and cards) used on the first day in the second round of focus groups, as at times they had proved to be a distraction to the participants. As an alternative, Reception children were given colouring pens and paper to focus their attention. Year 6 focus groups were run again allowing for free discussion, following on from issues and understandings they had raised in the initial session. The second round of focus groups, other than the changes already detailed above, followed the same sequence as they had on day one and lasted around 30 min. The recordings were transcribed combining the recordings from both days creating four transcripts, one for each group.

Data analysis

The data collected from all the focus groups was transcribed by the principal investigator, during this process the initial thoughts and ideas were noted down as this is considered an essential stage in analysis (Riessman, 1993). The transcribed data was then read and re-read several times and, in addition, the recordings were listened to several times to ensure the accuracy of the transcription. This process of "repeated reading" (Braun & Clarke, 2006) and the use of the recordings to listen to the data, results in data immersion and refers to the researcher's closeness with the data. Following on from this initial stage and building on the notes and ideas generated through transcription and data immersion is the coding phase. These codes identified features of the data that the researcher considered pertinent to the research question. Furthermore, as is intrinsic to the method, the whole data set was given equal attention so that full consideration could be given to repeated patterns within the data. The third stage involved searching for themes; these explained larger sections of the data by combining different codes that may have been very similar or may have been considered the same aspect within the data. All initial codes relevant to the research question were incorporated into a theme. Braun and Clarke (2006) also suggest the development of thematic maps to aid the generation of themes. These helped the researchers to visualise and consider the links and relationships between themes. At this point any themes that did not have enough data to support them or were too diverse were discarded. This refinement of the themes took place on two levels, primarily with the coded data ensuring they formed a coherent pattern, secondly once a coherent pattern was formed the themes were considered in relation to the data set as a whole. This ensured the themes accurately reflected what was evident in the data set as a whole (Braun & Clarke, 2006). Further coding also took place at this stage to ensure no codes had been missed in the earlier stages. Once a clear idea of the various themes and how they fitted together emerged, analysis moved to phase five. This involves defining and naming the themes, each theme needs to be clearly defined and accompanied by a detailed analysis. Considerations were made not only of the story told within individual themes but how these related to the overall story that was evident within the data. In addition, it was highly important to develop short but punchy names that conveyed an immediate indication of the essence of the theme. The final stage or the report production involved choosing examples of transcript to illustrate elements of the themes. These extracts clearly identified issues within the theme and presented a lucid example of the point being made.

Results

The thematic analysis process that was applied to the transcripts elicited key concepts that were evident in the data. These themes are viewed as essential in determining the understandings of all the participants. These categories have been labelled as "Knowledge through Education," "Role Models," "Fat is Bad," and "Mixed Messages." There are of course aspects of the participants' understandings that overlap across these categories. This, however, should be viewed as a good interpretation of understandings and attitudes in general, which are never made up of isolated concepts but are all relative to each other.

Knowledge through education

This theme is defined by the ability of all the participants to understand the roles of diet and physical activity. This is, in part, likely to be defined by different levels of education that the two age groups represented have, but nothing conclusive can be drawn given the relatively small sample size. The impact of their education on their knowledge will be demonstrated through evidence from the transcript.

All participants in the reception age group expressed the ability to name and identify different food items from the replica food. When they were asked to prepare a healthy lunch from the food items, they were able to point out food that would typically be classified as healthy.

However, despite displaying that they "know" what healthy means there is evidence of confusion, and it would seem the concept of something being "good" for them is interpreted to be things they like to eat. This suggests that they don't yet fully understand the concept of "healthy" food.

Their definition of healthy is centred on food they believe will make them grow for which fruit is highlighted as being particularly important. However, they also attribute this property to the food that makes up their personal diets. This understanding might result from being told to eat so they grow up to be big and strong. It is important to consider younger children's understandings are likely to be primarily shaped by their home environment, where the emphasis is often on how much children are eating as opposed to what they are eating.

This understanding of the reception-aged girls represented in this study of eating so they can grow up to be strong is also evident with the boys in the same age group. However, the reception boys also place great importance on the necessity of exercise to develop strength, this demonstrates another aspect in their knowledge.

It is fair to say Year 6 groups relished the opportunity to express their knowledge. They were able to identify and name different food groups and discuss different types of physical activity; what's more they understand the link between the two in relation to obesity. It seems other influences have impacted on the children's understandings' such as school and extracurricular groups.

The ability to identify a particular fruit by one of its universal characteristics shows a deeper level of understanding and suggests that a higher degree of learning. In fact it is explicitly stated that this nutritional knowledge has been gained at school.

Moreover, it isn't just a nutritional knowledge they have developed through education. They appear well versed in the concept of a balanced diet and also understand the importance of a balanced lifestyle in relation to physical activity. They are able to articulate the notion of a balanced, healthy lifestyle through a consideration of the consequences of over eating and not exercising.

I:

So what happens to you if all you do is you do watch TV and play the computer, eat the food that you told me was the bad food, what would happen to you?

*:

You would have a miserable life.

*:

Get fat, teeth will fall out.

Girls year 6

In the case of the Year 6 boys who took part in this study, it is apparent that although a great deal of their knowledge has come through education at school, other avenues have helped them develop different aspects of their understandings. In this case it seems to be through taking part in activities, typically sport outside of school, or and more uniquely to this group through the influence of their fathers.

It is evident where the ability exists, or is encouraged, to apply knowledge they have in a context relevant to their own lives, the knowledge becomes embedded in their understandings; it is applicable to them and, therefore, moves from being written on the board in school to being important to their own existence. This is exhibited by those participants, in particular the boys who participated, who have an involvement in sport. Having a motivation to understand nutrition and exercise leads to a desire to apply it because they comprehend the potential benefits. This aspect within the initial theme of knowledge through education leads directly on to the next theme of role models. The key difference between these two themes is the first relates to information that is directly and intentionally meant to inform the children about healthy lifestyles in an institutional setting, while the second theme is typified by understandings that are formed through interactions with other people.

Role models

The application of knowledge gained through education is often facilitated by role models such as family members who reiterate this information through example. Role models play an important role in the concepts described by all the groups, for example, the older boys reported that their fathers helped encourage healthy behaviours, above and beyond the nutritional knowledge in the previous theme.

*:

Like sometimes on an afternoon my dad goes to the gym, then there is these tracks outside, and I practice every day on my 100 meter sprint and I can do it in 12 seconds, and when I started doing it I was 21 second, so I keep practicing.

This demonstrates some of the participants' understandings have developed by examples set for them by significant individuals in their lives. This is evident in the younger children's understandings in a less explicit manner; the example below demonstrates good health behaviours can be established through everyday behaviour exhibited by role models.

There is some evidence that examples set to the girls who took part in this study, at home and by other role models, can encourage behaviours or ideals that are not beneficial to the girls health. Girls appear to look up to older female family members who aspire to be skinny.

*:

I like to be skinny, my nana does as well, and she wants to be skinny because she's fat now but I still love her.

Girls reception

They also appear to have developed unrealistic ideas about weight loss and the consequences in terms of treatment. Viewing hospital treatment as a solution to obesity, demonstrates a lack of understanding about the role of lifestyle behaviours in the condition. This may also suggest that these participants don't appreciate the importance of lifestyle behaviours in the onset of obesity.

*:

Guess what, I seen this film right the boy was fat right, his legs was right down to the bottom, he had a fat tummy, I was hiding cos I hated him, he was horrible, he will have to go to hospital, he was fat.

Girls reception

I:

So what would you tell somebody if you pretend that I was really, really fat, what would you tell me to do.

*:

Go to the doctors ... hospital, operation.

Girls reception

There was some evidence that the older girls in this study had a more balanced outlook on what sort of body shape was healthiest, because they were aware of the negative health consequences associated with being underweight. It is interesting, however, that they are aware that maintaining a healthy lifestyle may be a challenge and this may result in a barrier to adopting healthier practices.

Within the theme of role models, there was some evidence of a difference between the genders in terms of available role models. The participating boys often cited football heroes as people whom they looked up to and aspired to be like. This highlights the role of the celebrity in providing a role model for today's children; the evidence from the participants in this study may suggest that typically boys look to footballers and other sporting heroes. It can be argued that such individuals do not always provide a strong moral code; they are seen as following a healthy lifestyle in terms of diet and exercise. It would seem that the female participants in this study often looked up to celebrities who weren't so explicitly seen to be following healthy lifestyles, or a sense of caution was attached to following healthier behaviours.

Another interesting aspect of the notion of role models' is that the girls were more concerned with how they appeared in a physical sense; it was particularly striking that the Year 6 boys identified unhealthy behaviour in their female peers attributing this to a desire to be like models.

*:

Yes, she wants to be a model so she starves herself, her mam gives her a big packed lunch and she puts most of it in the bin, she's like that skinny then she walks out of the dinner hall.

There were many aspects of the transcript that highlighted participants were aware that being underweight was as worrying as being overweight. However, across the board they were far more critical of individuals who were overweight and discussed wide ranging consequences for these individuals, this leads on to the next theme evident in the analysis.

Fat is bad

There was a united consensus that being fat was something to avoid, that it was a bad thing, and had typically negative consequences. Elements of this theme have been demonstrated throughout the discussion of the previous two themes; however, this illustrates how their understanding impacts on their attitudes toward obesity.

In addition to the health issues and those relating to physical attractiveness were the issues of bullying and social exclusion, which seemed to play a big role in the children's understandings of what it would be like to be overweight. The stigma attached to being overweight is evident as participants often started giggling when talking about people being overweight.

Inability to have a successful career and even death were understood to be the results of obesity. Participants felt people who were overweight were in some way bad or an embarrassment. There was even a sense of fear toward people who they considered overweight, indicating that they would avoid being seen with somebody who was obese.

I:

So ... so what do you think about being fat, like if you see somebody in the street who looks like they are not very healthy do you think?

*:

They can't do much, like most of the things you want to do in life, like swimming, jogging.

*:

Jobs when you grow older.

Girls year 6

*:

Like if my parents were proper massive and I went to the town with them I would just say they took me to the town and I don't know them.

It is clear that the participants' understanding is that obesity is a very negative issue. However, there is also evidence that they understand the complexity of the condition and are also aware being underweight maybe as much of a problem. The older children in this study seemed to understand that it is a complex issue and fully grasped the concept of moderation. They often refer to the fact that you can have a small amount of things that maybe classified as unhealthy, as long as you don't eat them all the time or balance them out with exercise.

This category of Fat is Bad highlights an issue that clouds all the children's understandings of issues surrounding obesity and that is of conflicting messages. This notion of mixed messages forms the final theme evident in the data.

Mixed messages

The evidence presented here would suggest the information intended to educate and inform children is often met with equal amounts of contradictory or confusing messages and behaviours. The result of this is easily demonstrated by comparing what the children know they should be doing with what they actually talk about doing. For the majority of the participants their knowledge did not always match with their described behaviour, their food preferences often overriding their knowledge. This was perhaps not so surprising; knowledge does not by any means dictate behaviour.

Conflict existed in a number of forms in the understandings expressed by the participants. It is worth reiterating that the younger girls who participated believed treatment for obesity was to go to the hospital and have an operation-something they have picked up from a TV documentary-this conflicts with diet and exercise education they receive at school. Other participants gave more specific and direct examples of receiving contradictory information. This ranged from conflicts in direct health messages to conflicting information and action between school and home. They felt that at times it was difficult to know which information was the right information, not only was it conflicting but it was forever changing.

In addition to this, older children also pointed out they felt that healthy lifestyle information wasn't always delivered in the correct manner, there was a belief that stigmatising people who were overweight was negative. There was an awareness that there is a psychological aspect to overeating, and in some individuals it is this that needs to be addressed. Moreover, there was a feeling again demonstrated solely by the older participants that being overweight/obese could be difficult to rectify and maintaining a healthy weight could be a challenge.

This understanding of the complex nature of the obesity problem, coupled with the confusion and conflict in both the information and behaviours the participants are exposed to, can help explain some of the barriers to individuals adopting a healthier lifestyle.

Discussion

Comprehensive understanding

The results detailed above highlight some important findings as to how children understand obesity in terms of some of its causes and consequences. It was particularly clear that knowledge, often imparted in a school setting, is getting through to the children who participated in this study. However, it appears equally evident that this knowledge in many cases does not transfer to behaviour. Further examination of the results allows us to explore the potential reasons behind the knowledge-behaviour gap.

Role models by their nature provide examples for both the children's beliefs and their behaviour. There are a wide variety of potential role models for children from parents, teachers, peers, and celebrities. What seems particularly important, in terms of being a positive role model with regards to healthy lifestyles, is that children have an opportunity to view the process of being healthy. In this study, this was typified by the examples of the Year 6 boys who participated in sport with their fathers. It appears this close and active relationship allows the knowledge that has been started at school to grow. Allowing children the opportunity to apply their knowledge and see the steps taken by a role model to get or stay fit help translate this knowledge into behaviour. What is interesting, however, is that it seems passive behaviours by role models can have the same impact. It was the case with these participants that the effect of passive knowledge transfer seemed to be more negative, but that is by no means to say that passive behaviours by role models will not also encourage positive lifestyle behaviours in other cases. The most obvious example of this within this data set was the seemingly implicit messages that the girls received about being skinny. There was not an overtly explicit attempt on the behalf of the role models described here to encourage a "skinny" ideal; however, messages seemed to reach the participants that would indicate this is the case. The key difference between these active and passive role models appears to come from whether the role models place focus on the process; taking part in sport (in the example of the older boys) or outcome being skinny (in the example of the girls). Focus on the action of being physically active or enjoying a healthy diet in the case of these participants produces a healthier outlook on maintaining a healthy body weight. When that focus is on the outcome-the weight loss or the weight gain-there seems to be less concern for actually "being healthy" in terms of body weight and lifestyle. This notion about process and outcome is intrinsically linked to the theme of Fat is Bad.

It is interesting to note that whilst the children expressed an understanding of fat as a component of diet and were able to identify high fat foods and their link to obesity, the focus was on fat as an outcome and not so much about it as input. It is a well-documented fact that fat is a requirement of a balanced diet. The participants were able to recite in great detail the consequences of becoming fat but were not so forthright about the processes involved in becoming fat. It can be suggested that by focussing on the process of becoming fat and understanding the need for fat in moderation and being physically active it may help to discourage fat becoming the output. This may also help to draw away the focus from physical appearance that is so closely tied to the stigma attached to being overweight and place it on living a healthy lifestyle and being healthy.

The key finding of this study is that it is evident that children receive contradictory messages when it comes to following a healthy diet and taking part in exercise. The research presented here highlights children's understandings of some of the causes of obesity and the consequences of becoming overweight. However, it is equally evident that this information has reached them on a knowledge level but has not or cannot be fully translated into behaviour. It appears that central to this problem are the multiple discourses that exist around diet and exercise. Whilst government campaigns may impart facts and figures and provide advice on changes that can be made, there are a whole host of other sources to contend with. There is an undoubted role played by the media both in terms of active advertising campaigns for junk food or sedentary games and the passive portrayal of unattainable body shapes and sizes in magazines and by celebrity culture. However, more than this, health messages are competing against a variety of cultural values, social, and personal norms that may well go against messages that encourage certain behaviours. What is more is that ultimately individuals have the power and autonomy to make their own choices about diet and exercise. Stakeholders need to ensure that people are in a position to make an informed decision and not one where their judgement is clouded by an array of contradicting messages. There is also a responsibility to ensure that individuals are able to act on advice given and to provide advice that is relevant and tailored to individual circumstances. It is easy to understand why parents on a low income may struggle to incorporate "5 a day" into their families diets when they perhaps don't have access to a car and the nearest shop selling fresh fruit and vegetables is several miles away. Ensuring people know that frozen fruits and vegetables are just as good and, in some cases better, is a far more useful and usable message.

Comparisons with past research

The objective of this study was to explore children's understandings of obesity in terms of diet and physical activity; the children included were considered high risk because of their socio-economic status. To meet this objective, focus group data was analysed using thematic analysis. This analysis produced key themes pertaining to the understandings of the participants. There is not a wealth of prior research in this domain and it was for this reason thematic analysis was chosen to analyse the data. The method proved to be particularly useful in generating these exploratory data that are discussed here in relation to previous findings.

The theme of knowledge has previously been identified by Hesketh et al. ( 2005) in terms of information and awareness that is pertinent to children's perceptions of healthy eating, activity, and preventing obesity. Increasing knowledge relating to diet and physical activity cannot prevent obesity but it can encourage children to make informed choices.

This study, as have others (Hesketh et al., 2005; Borra et al., 2003; Musaiger, Mater, Alekri, & Mahdi, 1991), identified misunderstandings in children's knowledge as barriers to healthful behaviour. It might be useful to address this issue, particularly with younger children who are developing their knowledge. Previous literature has identified young children often consume their recommended daily intake of fruit but fall well short when it comes to vegetables (Dennison, Rockwell, & Baker, 1998). Government campaigns encourage people to eat five portions of fruit and vegetables a day ( www.5aday.nhs.co.uk); however, nutritionists would encourage three portions of vegetables and two of fruit-fruit having high sugar content. There was no evidence in the transcripts that any of the children were aware of or understood this distinction. This needs further investigation; however, education should encourage an understanding of fruit and vegetables as separate entities to help increase the consumption of vegetables (Gibson, Wardle, & Watts, 1998).

The evidence in this study suggests children grasp the causes of obesity, overeating, and low levels of physical activity; however, there was a general lack of understanding of the underlying physiological processes. There was a limited understanding of the concept of energy balance or that there might also be medical reasons for the obesity. Bell and Morgan ( 2000) demonstrated providing medical explanations for obesity can have a positive effect on children's attitudes to obese individuals. Overweight individuals were generally stigmatised by the participants in this study, so providing better medical information could help to alleviate these negative attitudes. It is fair to say those children who did have more in-depth knowledge of obesity were more sympathetic in their considerations of overweight individuals acknowledging the difficulty in making lifestyle changes.

The influence of parents concerning diet and exercise behaviours is well documented (Prout, 1996). Hesketh et al. (2005), Borra et al. (2003), and Young-Hyman et al. ( 2000) consider parental influence to be a determining factor in children's attitudes and understandings of obesity. It is clear this influence can be as detrimental as it can be beneficial. Previous research (Borra et al., 2003) argues interventions need to be developed that consider the role of the parent. Children cannot be expected to apply the information they receive at school to themselves if it is not reiterated at home. Nutritional education and physical education have not formed a core or extensive part of school curriculums in the United Kingdom in previous years, and there is now a generation of young parents who do not have the skills to attractively present appropriate foods (Tuttle & Truswell, 2002) or who regularly take part in sport themselves. The impact of this on their children's behaviour is that they don't always have examples of healthy behaviour to model their own on.

Of particular importance was the finding that children feel that they often receive mixed and contradicting messages. This is of great relevance when considering the development of policies and strategies that can be more effective. More over this backs up the findings of Dorey and McCool ( 2009) who conclude that nutritional messages evident in health promotion and advertising were often perceived by child audiences to be ambiguous. The authors warn that these contradictory messages could potentially serve to weaken the trustworthiness viewers have in health promotion initiatives. This really points to a key area in which health professionals can target efforts to tackle obesity. Clarity and consistency in healthy messages and recommendations are central to helping people take on board and act on the information they receive. Contradiction allows room for people to question the advice given and when effort is required to make a change in behaviour that change is less likely to be made if there is reason to doubt the accuracy of information. Furthermore, coherent messages need to consider person specific factors that may inhibit behaviour change; when individuals are encouraged to behave in a certain way but the constraints of day-to-day life lead to another, the results are confusion and hostility to the initial message (Owens & Driffill, 2008).

Procedural issues

The main methodological issue arising was participants from Reception struggled to engage fully in conversation, and the sessions followed a structure more a kin to an interview (i.e., question and answer). It was difficult to encourage responses that were longer than a few words; often one word responses were given. There is the potential to gain some very useful information from children in this age group; however, it can be a long and time-consuming process to elicit enough information to make the analysis process worthwhile. The length of the sessions also must be kept relatively short because attention spans are not long lasting; this was a finding similar to that of Miller ( 2000). The replica food items selected to help provide structure to the focus groups were useful and did provide a catalyst for discussion; however, for very young children (i.e., those in Reception) they resemble toys too closely, this then leads to them becoming more of a distraction, hindering the discussion. The use of the picture cards and pens and paper as suggested by Backett and Alexander ( 1991) provided a more a suitable means of structuring focus groups for young children.

There were at times issues with certain members of the groups making themselves heard more than others, thus the researcher had to encourage those happier to sit back and let others take the lead (Kirk, 2007). However, through a little encouragement all participants appeared comfortable talking with each other and participated equally, a result of the careful selection process. It also appeared to be beneficial speaking to boys and girls separately, with the boys often more excitable in their discussion style in comparison to the girls. It also facilitated the identification of some important issues, for example, the Year 6 boys identified eating behaviours present in the Year 6 girls that the girls themselves did not discuss.

Implications for the future

The Foresight Report (Department of Innovation Universities and Skills, 2007), in tackling obesity, points out that current policies are failing because they do not provide the depth and range of interventions needed. This present study has determined that central to children's understandings of the causes and consequences of obesity are the concepts of knowledge, the opportunity to apply this knowledge to their own lives, and the existence of role models to set an example. There exist certain myths and misconceptions that need to be addressed and children need to believe they can trust the health messages they receive because they are aware some messages are misleading or forever changing.

The key to this issue seems to be children learn by example, they can have all the knowledge in the world provided to them through an institution such as a school but this information needs to be supported by life at home. This provides evidence that campaigns need to target parents to tackle childhood obesity; this is an issue that policy makers are already aware of ( National Institute for Health and Clinical Excellence, 2006). However, this means health messages delivered to the general public need to be clearer and avoid ambiguity. There needs to be careful considerations of the context in which health messages are received, taking into account the understandings of the target population (Hesketh et al., 2005).

There were some issues raised in the focus group that were beyond the scope of this particular study. There was a representation of different ethnic minorities in the groups, and slight differences in the understandings of these different groups were identified. Further research should investigate the understandings of different minority groups to see if ethnicity influences or results in divergent concepts. Future study also needs to look at strategies that enable children to apply healthy lifestyle information to their own lives.

Children spend, on average, a quarter of their waking lives in schools; therefore, schools can be seen as an effective environment and source to help encourage healthy lifestyles. However, that leaves three quarters of a child's time in which they are out of the control of the school environment. Strategies must be developed to unite the teaching at school with practices in the home. This supports the conclusions of Hughes, Sherman, and Whitaker ( 2010) who write that strategies need to be framed in a manner that makes low income mothers feel more supported in addressing issues their children may have with their weight. Ensuring that approaches to encourage healthy lives take on a holistic format will also help to provide consistent and realistic role models. There needs to be a concerted effort from within society to develop role models who have a healthy relationship with food and exercise. These seem to already exist for young boys in the form of sporting heroes but seem in short supply for young girls who already consider that being healthy is the ideal but then look to surgery as a form of weight loss. Lieberman, Gauvin, Bukowski, and White ( 2001) highlight the importance of role models and peer influence in the onset of disordered eating in young girls and this needs to be seriously taken into account when sending out messages that being overweight is bad, girls need to be aware that being underweight also has severe health consequences.

In conclusion, the time children spend eating and taking part in physical activity out of school is likely to be the biggest challenge to preventing the continuing obesity problems in the United Kingdom, and this is where current strategies appear to be failing. Children understand obesity and its contributing factors in terms set out to them by those people they consider role models. It is only by helping these role models to provide consistent and reliable information by setting suitable active examples and by being aware of the impact of their passive actions that we can begin to address the problem of obesity.

Acknowledgements

The authors would like to thank Sunderland Children's Centres and Back on the Map for their support in facilitating this research.

Conflict of interest and funding

The author have not received any funding or benefits from industry or elsewhere to conduct this study

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Children's understandings' of obesity, a thematic analysis

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Childhood obesity is a major concern in today's society. Research suggests the inclusion of the views and understandings of a target group facilitates strategies that have better efficacy. The objective of this study was to explore the concepts and themes ...

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An article which makes a statement about the position of the researcher

How Race Becomes Biology: Embodiment of Social Inequality

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How race becomes biology: Embodiment of social inequality

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The current debate over racial inequalities in health is arguably the most important venue for advancing both scientific and public understanding of race, racism, and human biological variation. In t...

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An article which reports using focus groups as the method of data collection

Two Approaches to Focus Group Data Collection for Qualitative Health Research: Maximizing Resources and Data Quality

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This article discusses four challenges to conducting qualitative focus groups: (1) maximizing research budgets through innovative methodological approaches, (2) recruiting health-care professionals for qualitative health research, (3) conducting focus groups with health-care professionals across geographically dispersed areas, and (4) taking into consideration data richness when using different focus group data collection methods. In light of these challenges, we propose two alternative approaches for collecting focus group data: (a) extended period of quantitative data collection that facilitated relationship building in the sites prior to qualitative focus groups and (b) focus groups by videoconference. We share our experiences on employing both of these approaches in two national research programs.

What Is Already Known?

Despite the well-documented advantages of focus group data collection, modern challenges for qualitative health researchers exist. Health research funding is increasingly competitive, recruiting health-care professionals to participate in qualitative health research is challenging due to increasing busyness in clinical environments, vast geographical distances between research sites hinder data collection and consume valuable research resources, and finally, attempts to mitigate these challenges generate concerns of potential trade-offs that compromise data richness. These known issues pose significant challenges for qualitative health research focus group data collection.

What This Paper Adds?

The purpose of this article is to discuss these four challenges in the context of two nationally funded research programs that used focus groups as a data collection method with health professionals from busy clinical environments across geographically dispersed areas. We propose two different approaches to focus group data collection that may alleviate some of the barriers faced by qualitative health researchers while preserving data richness. These proposed approaches have the potential to save costs and alleviate some of the practical challenges researchers face in these contexts.

Background

Focus groups are a common qualitative data collection method and are considered an important qualitative health research technique ( Morgan, 1997), owing to their efficient and economical nature ( Krueger & Casey, 2000). Focus groups are defined as "group discussions exploring a set of specific issues that are focused because the process involves some collective activity" ( Kitzinger, 1994, p. 104). The key aspect of focus groups is the interactions between participants as a way of collecting qualitative data that would not emerge using other methods (i.e., individual interviews; Duggleby, 2005; Kitzinger, 1994; Peters, 1993). Focus groups yield large amounts of qualitative data and maximize face-to-face (FTF) participant-researcher contact compared to other qualitative methods ( Parker & Tritter, 2006). The choice of data collection method (i.e., individual interview vs. focus group) should be determined by the research question and purpose.

Despite the advantages of focus groups in qualitative health research, critical challenges exist. First, health research funding is becoming increasingly competitive as available grant dollar's decline ( Poulter &Young, 2011). This has a significant impact on grant budgets and resources allocated to data collection costs. Second, recruiting health-care professionals to participate in qualitative health research is an increasingly challenging process. The busyness of the clinical environment affects the availability of the staff, the rapport between researchers and staff, and the rapport among the staff themselves. The reality of busy clinical environments can result in a lack of time or ability to participate in research ( Hysong et al., 2013; Roxburgh, 2006). Third, conducting qualitative health research across geographically dispersed areas is difficult ( Sedgwick & Spiers, 2009), specifically due to lack of access due to large, physical distances between researcher and participants and the associated costs with bridging this distance ( Gratton & O'Donnell, 2011). Finally, to address cost, recruitment, and geographical issues, there is an increase in the use of technology for focus group data collection (i.e., Skype, Twitter, chat rooms, and videoconferencing). By shifting away from traditional FTF interaction, these modalities may have implications on data richness.

We discuss these four challenges in the context of two nationally funded research programs that used focus groups as a data collection method with health professionals from busy clinical environments across geographically dispersed areas. The main purpose of this article is to propose how two alternative approaches to focus group data collection alleviate some of the main challenges and barriers faced by qualitative health researchers. The two approaches are (a) an extended period of quantitative data collection that facilitated relationship building in the sites prior to qualitative focus groups and (b) the use of videoconference to conduct qualitative health services research.

The Research Studies

Project 1: Translating Emergency Knowledge for Kids (TREKK)

The TREKK project is a multiyear, pan-Canadian study. The goal of TREKK is to improve pediatric emergency care by ensuring the application of best research evidence in general emergency departments (EDs) without pediatric specialists. The first phase of this project, the Needs Assessment, investigated knowledge needs and preferences of two key groups: health-care providers working in general EDs and parents seeking care for their children in these EDs. To accomplish this, quantitative electronic surveys were conducted on iPads with both groups in 32 hospitals from May 2012 to July 2013 ( S. D. Scott et al., in press; S. D. Scott, Albrecht, Given, Arseneau, & Klassen, 2016). In 7 of the 32 participating sites, survey data collection was followed by targeted qualitative focus group data collection during intensive site visits, ranging from 2 to 4 days in length. The site data collectors and Needs Assessment Coordinator conducted these visits together from June 2013 to December 2013 to obtain further clarity and granularity of the survey results. These sites were chosen for maximum variation and to represent urban, rural, and remote regions.

Establishing Relationships Prior to Focus Group Data Collection

Given that the project was taking place at 32 hospitals across the country, regional, on-the-ground staff routinely visited the individual sites over the course of the study. Each data collector visited between one to four sites multiple times, and the Needs Assessment Coordinator visited seven sites over the course of 19 months. This strategy served two purposes: (1) to maximize recruitment of health-care providers and parents in a busy clinical environment and (2) to overcome technology barriers to participation. During the site visits, the data collectors spoke to staff and parents about the research project, the local environment, and other related topics. Some of the data collectors were also nurses working at the regional pediatric ED and could offer advice to staff or share expertise in the research sites. Often the data collectors distributed e-mails or posters to announce their visits and brought small incentives such as coffee, homemade baking, or stickers for children coming into the EDs. These interactions served to build relationships between the research team and the participating sites, which would hopefully result in richer qualitative data and higher participation rates for focus groups, despite the busy, and often stressful, clinical setting.

Project 2: The Implementation and Evaluation of an Alberta Childhood Asthma Clinical Pathway (CP)

The Implementation and Evaluation of an Alberta Childhood Asthma CP is a nationally funded research project in Canada. The main goal of this research project was to improve pediatric asthma care in general EDs by implementing and evaluating a research-based CP. One phase of this multiphase project was a process evaluation, which focused on understanding the factors that affect the use of CPs in clinical practice and explored the barriers and facilitators that shaped the CP implementation process. To accomplish this, focus groups were conducted with multidisciplinary, health-care professionals in 5 of the 17 EDs that implemented the asthma pathway. Focus groups enabled us to observe group dynamics and levels of consensus and to gather information on contextual factors that may shape the implementation process ( Krueger, 1994). Thus, for our research purpose, focus groups were the best method of data collection to capture these insights. Focus groups were conducted at two time points: pre- and postpathway implementation. The five EDs were in rural regions of Alberta with extensive patient catchment areas and busy clinical environments. The preimplementation focus groups were conducted in-person FTF, which presented many challenges. We experienced fixed focus group scheduling due to our restricted and specific travel dates to these rural sites. This form of scheduling did not allow for any flexibility to accommodate unpredictable changes (e.g., emergency situations, patient load, and staffing levels) that can occur in an ED environment.

Use of Videoconferencing to Conduct Focus Groups

In response to these challenges, the research team decided to use videoconferencing technology as an alternative approach to conduct the postimplementation focus groups. Videoconferencing has become an increasingly used communication and education modality in health care ( Daley, Spalla, Arndt, & Warnes, 2008; Harris, Smith, & Armfield, 2007; Winters & Winters, 2007). It has been used to provide health information and education to remote areas ( Collie et al., 2007; Locatis et al., 2006) and education to health professionals ( Harris et al., 2007). Videoconferencing has also been used as cost-saving strategy for interviews ( Shore, Brooks, Savin, Manson, & Libby, 2007). The research team decided that videoconferencing would allow for more flexible scheduling at the convenience of the staff with less cost.

We conducted the postimplementation qualitative focus groups using Alberta Health Services "Telehealth" videoconferencing technology. Telehealth is a secure technology in health-care facilities across Alberta that is currently used to connect health-care professionals to each other and to patients ( Alberta Health Services, 2017). Telehealth provides a data collection mode for qualitative health researchers that is in real time, is safe and confidential, and provides the technological support for scheduling and use. Alberta has over 1,600 telehealth services with the ability to reach out to sites both nationally and internationally ( Alberta Health Services, 2017). Telehealth enables people to see, hear, and talk in a virtual environment. Using an established videoconferencing program allowed us to collect data in real time, this enabled interaction between and among the participants and the researcher ( Tuttas, 2015). In the literature, there are some concerns that videoconferencing may not provide the same level of interaction between the participants and moderator as FTF focus groups ( O'Conaill, Whittaker, & Wilbeur, 1993); however, this evidence primarily compares FTF groups to online text-only focus groups and not technologies that incorporate audiovisual capacity to mimic FTF focus groups.

Project 1: TREKK

The cost breakdown of the focus group data collection for the TREKK research project is described in Table 1.

In total, 57 health-care professionals participated in 13 focus groups in the seven sites. The average number of participants per focus group was five. Focus groups ranged in length from 30 min to 1 hr 20 min, depending on the number of participants present. Participant demographics are described in Table 2.

Project 2: The Implementation and Evaluation of an Alberta Childhood Asthma CP

The cost breakdown for the Partnership for Health System Improvement (PHSI) research project is described in Table 3.

In total, 52 health professionals participated in a total of 9 focus groups, 1 individual interview, and 2 dyad interviews across five sites. Focus groups and interviews ranged in length from 30 min to 1 hr. Participant demographics for the preimplementation focus groups are described in Table 4. We collected the postimplementation data from five focus groups and one dyad interview at the five ED's 6-12 months postimplementation of the CP. We conducted the postimplementation focus groups and dyad interview via Telehealth videoconferencing. Participant demographics for the postimplementation focus groups are described in Table 5. There was no decrease in sample size from the preimplementation to the postimplementation focus groups.

To assess data richness for PHSI, we collected field notes and observations regarding participant interactions, and we coded these interactions using qualitative content analysis. We did not note any decrease in the amount and depth of interaction from the preimplementation versus the postimplementation focus groups. Quantitatively we assessed data richness by comparing the FTF focus groups versus the online audiovisual (teleconferencing) focus groups by (a) the number of participants, (b) the average length of interview time, (c) the average number of words per transcript, and (d) the average length of participants' longest response ( Table 6).

Discussion

Challenge 1: Funding Challenges for Health Services Research

Some would argue that data collection costs should not present as a challenge to the researcher if the outcome is rich qualitative data; however, it is an unavoidable issue considering the declining rates of health research funding ( Poulter & Young, 2011). In the context of the TREKK project, data collection resources were maximized due to TREKK's focus on rural and remote regions of the country. There were additional financial costs associated with conducting regular relationship building activities, for example, research staff time, travel costs, providing incentives, and so on, that are not reflected in the data collection expenses outlined in Table 1; however, this was a worthwhile approach for the TREKK project and may also be valuable expenditures for other long-term projects with high demands on participants, such as multiple phases of data collection over time.

For the PHSI research project, the use of videoconferencing was more efficient and economical than the preimplementation FTF focus groups. Rupert, Poehlman, Hayes, Ray, and Moultrie (2017) found in a comparison of FTF, live chat, and video focus groups that platform costs for the online and video focus groups were higher and that overall the differences in costs between data collection modes were minimal. However, comparing the costs of both approaches within this project, we saved a total of $2,722.83 CAD using the videoconferencing modality. The reasoning for such differences is that our platform to host the videoconferencing focus group was free. Abrams, Wang, and Galindo-Gonzalez (2015) noted a variety of advantages with online audiovisual and online text-only focus groups that save money and time such as no need to buy audio or visual equipment, no need to find a physical location, and no need to hire transcribers. A number of technologies (i.e., iPads, Twitter, and Telehealth) offer proven examples of data collection methods that are cost-effective, efficient, and employ modern technology ( S. D. Scott et al., 2016).

Challenge 2: Recruiting Health-Care Professionals to Participate in Qualitative Health Research

Recruiting health-care professionals to participate in research is a time-consuming process fraught with contextual barriers when engaging busy health-care professionals ( Hysong et al., 2013). There is a need for qualitative researchers to maximize existing resources, "think outside of the box" to recruit participants, and collect data. Flexibility, communication, and good working relationships are paramount to research recruitment ( Newington & Metcalfe, 2014). Participant recruitment is a primary concern for researchers because low participation rates can have negative consequences on the research itself ( Erwing et al., 2004). Our proposed approaches demonstrate two different ways to recruit health professionals to participate in, often time-consuming, qualitative research.

For the TREKK project, engaging with sites early and regularly to establish trust and rapport prior to focus group data collection resulted in excellent focus group participation, despite the short time frame for qualitative data collection in each site. Participant recruitment was efficient and effective because preliminary contact and relationship building served to identify key contacts to help with room booking, advertising times and locations for focus groups, and reminding ED staff about the project and the focus groups as data collection dates approached. This resulted in high participation ( N = 57) in the TREKK focus groups, leading to excellent representation across demographic variables, demonstrated in Table 1.

From our PHSI research experience, videoconferencing accommodated the needs of the participants by increasing scheduling flexibility. There is increasing interest in the use of videoconferencing technology in health-care practice, education, and research due to its economic feasibility ( M. Smith & Leigh, 1997; Zerr & Pulcher, 2008). It is an expanding technology that provides an alternative to FTF focus groups in research contexts where participants are widely geographically dispersed and eliminate barriers to access ( Sedgwick & Spiers, 2009). Online focus groups such as chat rooms and videoconferencing have the potential to reach a more diverse range of participants across geographically dispersed areas than FTF focus groups ( Rupert, Poehlman, Hayes, Ray, & Moultrie, 2017). Technical problems are recognized as a disadvantage to online audiovisual and online text-only focus groups ( Abrams et al., 2015). Yet, in our experience, the videoconferencing platform that we used (Telehealth) provided a good quality service that was efficient and reliable with no sound or technical problems. It provided a secure environment that did not affect the confidentiality of participants.

Challenge 3: Conducting Focus Groups with Health-Care Professionals Across Geographically Dispersed Areas

Important factors that affect the conduct of research across geographically dispersed areas include time, distance, building relationships, gaining administrative approval to conduct the study, advertising the study for participant recruitment, access to available participants, and budgetary requirements for travel. This foundational work may be done over e-mail or the telephone; however, in busy clinical environments, like EDs, it may be best done in-person.

In the context of the TREKK project, this foundational work occurred gradually through with local contacts prior focus group data collection, which enabled long-distance travel for data collection. It also created the opportunity to conduct focus groups in rural and remote sites that are often left out of research due to the added complexities of accessing these sites and ensuring sufficient participation to justify the expense of reaching these locations.

In the PHSI project, videoconferencing technology provided an active response to overcome the challenges of recruitment, distance, and cost when conducting focus groups in busy rural clinical environments. It also enabled greater flexibility for date/time of data collection. This confirms the previous findings of Reid and Reid (2005) who found that the factors of cost, time, and location savings were the most obvious advantage of online focus groups and Abrams et al., (2015) who stated that greater active participation of diverse people is an advantage of audiovisual and online text-only focus groups.

Challenge 4: Data Richness Across Focus Group Data Collection Methods

In the TREKK project, focus groups were conducted using the traditional FTF approach that has been deemed the most optimal for data richness ( Abrams et al., 2015). Over the course of 13 focus groups with 57 participants, we were able to collect rich descriptive data that answered our research question. These data complemented the quantitative survey data obtained from health professionals ( N = 1,471) and health consumers ( N = 897) and provided clarity and explanation of some of the unexpected and/or interesting results.

In the PHSI research study, data richness was compared between the preimplementation FTF focus groups and postimplementation audiovisual (videoconferencing) focus groups using qualitative and quantitative approaches.

Previous studies have reported varied results on whether online audiovisual focus groups (i.e., videoconferencing, Skype; Abrams et al., 2015; Kite & Phongsavan, 2017; Sullivan, 2012) versus online text-only focus groups (i.e., Twitter, chat room) can achieve the same degree of data richness compared to FTF focus groups ( Bruggen & Willems, 2009; Moore, McKee, & Mc Loughlin, 2015; Schneider, Kerwin, Frechtling, & Vivari, 2002; Underhill & Olmsted, 2003). Videoconferencing as a research tool has great potential, reaching geographically dispersed populations, increasing sampling, and achieving interaction that is similar to FTF focus groups ( Kite & Phongsavan, 2017; Sullivan, 2012). Online audiovisual and online text-only focus groups have the advantage of saving money and time and attracting diverse participation in comparison to FTF focus groups ( Abrams et al., 2015). Schneider, Kerwin, Frechtling, and Vivari (2002) found that FTF focus group participants are more likely to discuss their opinions and insights in detail than those participating in online text-only focus groups, and Moore, McKee, and Mc Loughlin (2015) found that lack of human interaction was the main limitation to online text-only focus groups. In contrast, Underhill and Olmsted (2003) found that participants in an online text-only focus group produced a similar amount of topic-related comments and ideas as participants in an FTF focus group.

Complementary to the findings of Abrams et al., (2015), we found that the videoconferencing offered similar data richness to FTF focus groups. Abrams et al. (2015) was the first study to compare data richness between FTF, online text-only, and online audiovisual focus group mediums using an adapted version of Ogden and Cornwell's (2010) five dimensions data richness criteria: length of response, emotional/descriptive expression, "the use of personal pronouns," descriptions of action, and "expressions of insight and causation" (p. 1064). These dimensions offer a means to systematically assess data richness between focus group modalities, which is increasingly important in this era of online communication and technology advancement.

Videoconferencing provided rich qualitative data that also saved time and cost. Real-time videoconferencing enables verbal and nonverbal cues of expression to be collected, contributing to a greater in-depth analysis of the data ( Glassmeyer & Dibbs, 2012). However, technical problems (i.e., not seeing video at both locations, poor audio quality, etc.) are a potential drawback for online audiovisual focus groups ( Sullivan, 2012). We had information technology employees available at the researcher's and participating sites to ensure that the technology worked and to resolve any potential issues. As recommended by T. M. Smith (2014), we carried out a trial run of the videoconferencing technology prior to each focus group and built in extra time to scheduling in case any technological issues arose. As demonstrated in Tables 4 and 5, the use of videoconferencing did not affect participant representation and data quality. We used real-time transcription for data collection. Real-time transcription by court reporters increases accuracy, provides a faster turnaround time of transcripts, decreases distraction for focus group facilitators, and is more convenient ( S. Scott et al., 2009). In comparison to Kite and Phongsavan (2017), we did not have issues with sound quality, and the use of real-time transcription mitigated this potential challenge.

All participants were familiar with using this virtual mode of communication. As recognized by Abrams et al. (2015), participants' familiarity and self-efficacy with the audiovisual communication can impact data quality. Our choice to use Telehealth videoconferencing allowed participants to communicate FTF in real time in a familiar setting at the participating sites. This created a comfortable environment and enabled interaction among participants adding to data richness.

Future Research

There were many benefits to investing time and energy in relationship building prior to data collection in the TREKK project including maximizing resources, enhancing participation, and conducting research in diverse regions. Future research is needed to examine the costs of relationship building to guide research planning and budgeting. Comparing research outcomes to data collection investments (e.g., financial, resource) would also help to guide qualitative health research protocols.

Recruitment of health professionals for health research is a well-documented challenge ( Johnston et al., 2010; Sahin, Yaffe, Sussman, & McCusker, 2014). However, the evidence base to identify the most effective recruitment strategies is weak. As Bower and colleagues (2009) highlight, "recruiting for science is not underpinned by a science of recruitment" (p. 393). Future effectiveness research is needed to identify effective recruitment strategies for qualitative health research.

Despite the benefits of using videoconference technology to conduct focus groups, it is important to consider the following issues: the research context, the preference of the participants, the researcher-participant relationship, technical issues, the confidentiality and consent of the participants, recording and transcribing, and the availability and accessibility of the videoconferencing service. From our research experience, videoconferencing provides a viable solution for conducting focus groups in certain research contexts. Examining the influence of participants being able to see themselves on screen in the online audiovisual focus group would be useful, and the impact that familiarity with the technology has on participation and data quality are all important areas for future research.

An in-depth comparison of data richness between the FTF and audiovisual focus groups was beyond the purpose of this article. Future research could examine participant thoughts and preferences of these modalities. Future research could also apply Abrams et al., (2015) systematic criteria to measure data quality: (a) amount of topic-related data, (b) amount of unrelated data (no theme, socializing, medium related), (c) researcher ratings of data richness, and (d) word count and linguistic characteristics of data to focus groups conducted using multiple modalities within the same research project.

Conclusion

These proposed approaches to focus group data collection yielded strong participation rates, rich qualitative data, and cost savings. The two approaches to focus group data collection have methodological contribution to the current evidence base of focus groups in qualitative health services research. While an extended period of relationship building prior to in-person focus group data collection and videoconferencing are two very different techniques, both approaches are particularly effective and should be considered for research in geographically disperse areas, rural and remote research, and busy clinical environments.

Authors' Note
Ms. Flynn is supported by an Alberta Innovates Clinician Fellowship. Ms. Albrecht is supported by an Alberta Innovates Graduate Studentship and a Women and Children's Health Research Institute Graduate Studentship. Dr. Scott holds a Canada Research Chair (Tier II) for Knowledge Translation in Child Health and a Population Health Investigator award from Alberta Innovates (formerly Alberta Heritage Foundation for Medical Research).

Ms. Flynn also acknowledges Knowledge Translation Canada (past doctoral trainee) and the Women and Children's Health Research Institute (past doctoral trainee) for their support of her doctoral research.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Implementation and Evaluation of an Alberta Childhood Asthma Clinical Pathway is a Canadian national research project funded by the Canadian Institute for Health Research. Translating Emergency Knowledge for Kids is funded by the Networks of Centre's of Excellence Knowledge Mobilization Initiative and supported either financially or through in-kind contributions by the following agencies and institutions: Pediatric Emergency Research Canada, Children's Hospital Research Institute of Manitoba, University of Manitoba, Research Manitoba, ManitobaCPD.com, Cochrane Child Health Field, Alberta Children's Hospital Research Institute, Children's Hospital of Eastern Ontario, Ontario ED Local Health Integration Network Leads, Alberta Health Services, and Women and Children's Health Research Institute.

ORCID iD
Rachel Flynn http://orcid.org/0000-0003-2980-7441

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# data[].attributes.attachment_link.title
Two Approaches to Focus Group Data Collection for Qualitative Health Research: Maximizing Resources and Data Quality - Rachel Flynn, Lauren Albrecht, Shannon D. Scott, 2018

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This article discusses four challenges to conducting qualitative focus groups: (1) maximizing research budgets through innovative methodological approaches, (2) recruiting health-care professionals for qualitative health research, (3) conducting focus groups with health-care professionals across geographically dispersed areas, and (4) taking into consideration data richness when using different focus group data collection methods.

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An article which reports using observation as the method of data collection

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An article which reports using interviews as the method of data collection Global marketing strategy in professional sports. Lessons from FC Bayern Munich

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This study analyses the global marketing strategy developed by FC Bayern Munich, one of the most successful football clubs in terms of athletic achievements, financial performance and number of worldwide supporters. It aims to shed light on the efficacy of the club's management in addressing the demands of the existing stakeholders as well as its rapidly expanding global fan base among other aspects. To achieve these goals, an in-depth analysis based on the study of technical reports, official websites, commissioned consultancy studies and surveys has been conducted. A series of interviews with academics and supporters was also conducted. Findings reveal that FC Bayern Munich has already taken strides towards addressing the needs of its original local supporter-owners as well as those of its global fans. Other points discussed include strategies to increase the target market, to become more international and to close important deals and sponsorships.

# data[].attributes.attachment_link.title
Global marketing strategy in professional sports. Lessons from FC Bayern Munich

# data[].attributes.attachment_link.description
This study analyses the global marketing strategy developed by FC Bayern Munich, one of the most successful football clubs in terms of athletic achievements, financial performance and number of worldwide supporters. It aims to shed light on the efficacy of the club's management in addressing the demands of the existing stakeholders as well as its rapidly expanding global fan base among other aspects.

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An article which mentions a null hypothesis

Null hypotheses for developmental evolution

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ABSTRACT

How much evolutionary change in development do we expect? In this Spotlight, we argue that, as developmental biologists, we are in a prime position to contribute to the definition of a null hypothesis for developmental evolution: in other words, a hypothesis for how much developmental evolution we expect to observe over time. Today, we have access to an unprecedented array of developmental data from across the tree of life. Using these data, we can now consider development in the light of evolution, and vice versa, more deeply than ever before. As we do this, we may need to re-examine previous assumptions that appeared to serve us well when data points were fewer. Specifically, we think it is important to challenge assumptions that change is very rare for all developmental traits, especially if this assumption is used to sustain an erroneous view that evolution always optimizes adaptive traits toward increasing complexity.

Introduction

Modern developmental biology is firmly grounded in evolutionary biology. The principle that all cellular life shares a common ancestor underlies our hypotheses about how the genome interacts with non-genetic parameters to direct cell behavior during development, homeostasis and disease. When we choose model organisms to study development, we often base these choices not only on ease of laboratory maintenance, but also on their evolutionary relationships to other organisms. We do this because we believe that different organisms might teach us different things about the processes we study. Furthermore, we frequently interpret our results by asking to what extent the processes we observe in one organism are likely to be operative or 'conserved' in others. When we perform, for example, a gene ontology (GO) annotation to infer the functions of genes, we are hypothesizing that molecular functions experimentally demonstrated in one organism can predict the untested functions in other organisms, based on an assumption of evolutionary conservation.

At this stage in our field, it is appropriate to ask whether we are robustly applying evolutionary assumptions to the study of development. It has been 125 years since the publication of the first volume of Archiv für Entwicklungsmechanik der Organismen, a foundational journal that in some sense marked the rise of an experimental, rather than observational, approach to understanding development ( Counce, 1997). A more recent conceptual shift came with the establishment of 20th century 'Evo-Devo': the formalization of the concept that the process of development is a crucial component of the evolutionary process, because it generates phenotypes upon which natural selection may act ( Raff, 2012). Increasing numbers of taxa are now emerging as 'new' model organisms, providing both an opportunity and a challenge: how to compare data collected from developmental experiments across organisms, while considering their evolutionary history.

Developmental biologists make these comparisons because we are often interested both in developmental mechanisms, and in the similarities and differences between these mechanisms and their counterparts in other organisms. Evolutionary developmental biologists make this query explicit: we try to determine the evolutionary origins and dynamics of these mechanisms. We frequently approach this by appealing to a hypothesized ancestral state, and use this hypothesis to propose the direction of evolutionary change, distinguishing between 'ancestral' and 'derived' mechanisms. Testing these hypotheses is challenging because the amount of data we have access to, although growing, pales in comparison with the unobserved data from taxa not yet studied. Nevertheless, we believe there are fruitful ways for developmental biology to place its findings in an increasingly robust evolutionary context.

With this Spotlight, our goal is to highlight recent advances in the methodological rigor of evolutionary biology that are highly relevant to the study of development. Specifically, we suggest the approach of defining a null hypothesis - a hypothesis for how much developmental difference we would expect to observe between organisms - by using evolutionary and developmental data to inform our estimates of relevant parameters ( Table 1). With this framework, we encourage comparative developmental biologists to interrogate the assumptions we invoke when formulating hypotheses, designing experimental approaches and interpreting results. We believe this approach will help us refine our questions, from 'How are developmental mechanisms different across taxa?' to 'Are developmental mechanisms more or less different than we would expect?'.

Inferring evolutionary change for developmental characters

Comparisons of developmental characters often share a few qualities that can make evolutionary inferences challenging. These comparisons, e.g. between fruit flies and mice, frequently span hundreds of millions of years in evolution ( Jenner, 2006). This is due to the historic choice of taxa to study in developmental biology, as well as an intentional choice of lineages likely to capture variation in developmental features ( Jenner and Wills, 2007). In addition, these comparisons may include very few organisms (often fewer than ten taxa, possibly as few as two). This is generally because collecting developmental data is expensive, and requires substantial expertise as well as lab-amenable organisms.

Making an evolutionary inference over deep divergences and using few taxa is a notoriously difficult type of analysis ( Freckleton, 2009; Wagner et al., 2000). This is not to say that inferences for developmental data are impossible, rather that care must be taken to avoid common pitfalls and to not over-interpret the results ( Swofford and Maddison, 1992). In cases where data points are few, we are tempted to use the method perceived to be the most simple or intuitive. One commonly used approach is parsimony, a method of inference that, in its most strict application, seeks to minimize the total number of evolutionary changes required to explain extant distributions of traits ( Joy et al., 2016). However, there are risks associated with this application, of which we consider two to be the most crucial.

Considering rate, reversals and convergence

We can expand the repertoire of approaches we use to compare developmental data across species by incorporating ideas from the field of phylogenetic comparative methods, which has developed a robust literature on models of character evolution ( Felsenstein, 1985; Garland and Ives, 2000; Lewis, 2001; Swofford and Maddison, 1992; Tarasov, 2019). These include methods of inference that allow for unequal probabilities of transitions between character states ( Cunningham et al., 1998; Swofford and Maddison, 1992), changes in the rate of trait evolution ( Rabosky et al., 2014), uncertainty in our observations ( Garland and Ives, 2000), missing characters and missing taxa ( Maddison, 1993), all of which are common challenges faced by developmental biologists. For discrete characters, many of these methods have their roots in a Markov framework of evolutionary change. Under this framework, the probability of change depends only on the current state, and can be calculated using a matrix that describes the rate at which evolutionary transitions occur ( Swofford and Maddison, 1992). Even if we choose not to employ these methods mathematically, we can incorporate the theoretical underpinnings of a transition rate-based approach into our evolutionary interpretations of developmental data.

Consider the example shown in Fig. 2. In this hypothetical scenario, we have generated new data on the expression of a transcription factor during comparable developmental stages across three taxa. In studies of a fly and a beetle ( Drosophila melanogaster and Tribolium castaneum, respectively), both express the gene during stage 1 of development but not during stage 2, whereas another fly ( Clogmia albipunctata) shows the reverse pattern of expression. However, we are not limited to making inferences based only on our new gene expression data: from previous studies, we also know the following salient details about these organisms and their evolution: (1) the amount of shared evolutionary history that we are considering when comparing these three taxa is ∼750 million years (the total sum of all shared branch lengths); (2) these three taxa are a very small sample of the set of all extant insect species, the shared evolutionary history of which sums up to billions of years; and (3) the results suggest that at least one change in expression timing has occurred within our relatively small sample of evolutionary history; there may have been more than one change (under a non-parsimonious hypothesis), but not zero changes.

We might be tempted to hypothesize that the shared expression during stage 1 in T. castaneum and D. melanogaster represents the ancestral timing of expression in the most recent common ancestor. Under this hypothesis, expression during stage 2 in C. albipunctata would be the derived state. However, our data could also be described by multiple alternative scenarios ( Fig. 2; Table 2). Deciding whether any of these scenarios is better supported than the others requires us to examine our a priori assumptions about the tempo of developmental evolution.

It might be that across the entire insect tree, there was exactly one change in the timing of expression since the common ancestor ( Fig. 2, scenario A), and our study happened to sample that single change along the branch leading from the common ancestor of flies to C. albipunctata. In this case, the observation of one change in the sample size of 750 million years is not representative of the broader pattern of evolution of this trait. In other words, our study happened to sample a very rare change by chance, even with a sample size that provided a very small window into the total evolutionary history of insects.

Alternatively, we can consider scenarios in which our sample of evolutionary history is representative of the broader distribution ( Fig. 2, scenarios B-D). In scenario 2B, one change per 750 million years is approximately representative of the rate of character transitions for the rest of the tree. Given that the full tree contains billions of years of total evolution (summing over all branch lengths), this scenario predicts many more than the single change hypothesized in scenario 2A. We note that, when comparing the three study taxa only, the interpretations proposed by scenarios 2A and 2B are identical ( Fig. 2A,B, upper trees), and both are equally parsimonious; it is only when we expand our view to include the unobserved taxa in the full tree of extant insects ( Fig. 2 A,B, lower trees) that the difference in assumptions underlying these becomes apparent.

In scenario 2C, the rate of character transitions is greater than one change per 750 million years. This scenario predicts that our observed data are explained by more than one change, and therefore includes some instances of either convergence (independent evolution of the same timing of gene expression) or reversal to the ancestral state of pathway activity after evolution to a derived state. This scenario is not strictly parsimonious, but is entirely plausible for many developmental traits.

Finally, in scenario 2D, convergence and reversals occur at such a high rate that they are the rule rather than the exception. Under this scenario T. castaneum and D. melanogaster share the same character state simply by chance. In both scenarios 2C and 2D, because a large and unknown number of changes have occurred, our ability to infer the ancestral state comes with a considerable amount of uncertainty.

In the absence of additional information beyond our observed expression data, the evolutionary history described above suggests that scenario 2A is less likely than the others. This is because 2A is the only scenario that requires the interpretation that our small sample of evolutionary history ( Fig. 2A, upper tree) happens to include a change that is very rare with respect to the entire tree ( Fig. 2 A, lower tree). In other words, the variation that is present even in the small sample we have suggests that the rate of evolution is greater than the near-zero rate that would be required in scenario 2A. Scenarios 2B-D seem more consistent with the information we have gathered. However, distinguishing between them is difficult because our small sample size cannot tell us whether the rate of transitions between character states (in this case, the timing of expression) is low, high or somewhere in between.

Given the depth of divergence and the small sample, we might despair at the thought of being able to draw any inferences about the ancestral state with these data, because it might seem that almost any possible evolutionary rate is plausible. However, we argue that developmental biology is precisely the discipline that can help us make this distinction. We can contextualize our prediction about the frequency of evolutionary change by considering data collected in laboratory studies of development, including the nature and complexity of the transcriptional regulatory machinery, the degree of genomic sequence conservation across taxa, the degree of pleiotropy and the amount of standing variation in gene expression within a population. These data could help guide our ability to suggest one scenario as being more likely than the others. For example, if we observe that expression timing is variable within a population, is controlled by few regulators and that the sequences of these regulators have little conservation across taxa, then a high rate of evolutionary change in expression timing (scenario 2D) becomes increasingly plausible.

Character state space

The examples we have considered thus far describe traits as having one of two possible states: either functional during a developmental process or not ( Fig. 1); or expressed during one of two developmental stages ( Fig. 2). In reality, a character like the timing of gene expression will have many possible states that cannot be captured in a single binary comparison. Given this, we should think critically about how we describe character state space, which is the set of possible transitions between character states that can be realized during evolution ( Hoyal Cuthill, 2015; Swofford and Maddison, 1992).

In a transition rate matrix format, the character state space is determined by the number of rows and columns, representing the number of possible evolutionary changes from any single state (e.g. the Mk model) ( Lewis, 2001). Thus, for the example in Fig. 2, the full range of evolutionary possibilities would be best described by an expanded matrix that allows for a larger number of transitions, such as expression in earlier or later stages, or the loss of expression of those genes entirely. Considering an expanded set of possible transitions can help us appreciate that if the character space is large enough and the rate of shifts is small but non-zero, it is plausible that the common ancestor could have shown a character state not observed in our extant taxa at all.

Errors in inference can result if we limit ourselves to constructing binary state comparisons based on similarities or differences to a presumed 'ground state', especially if that ground state is based on a biased view of animal complexity ( Beaulieu et al., 2013). For example, it may be tempting to assign the 'ground state' to the organism first studied or most well studied, and to then compare all other organisms to this standard. Such a comparison, however, can mask the hidden differences in unobserved states among non-model organisms, painting a skewed view of evolution that appears to show 'increasing complexity' toward well-studied lineages for which we have more observations ( Dunn et al., 2015).

We therefore suggest that keeping in mind the likely existence of developmental character states that are thus far unobserved may improve experimental design and data interpretation. Phylogenetic comparative approaches that take into account unobserved or hidden states are one of the most active frontiers of research in evolutionary methods ( Beaulieu et al., 2013; Tarasov, 2019; Uyeda et al., 2018). This is particularly relevant to the fields of development and Evo-Devo, as complex character structures involving ordered matrices, hidden states and hierarchical interactions between transitions likely describe the evolution of most developmental mechanisms ( Beaulieu et al., 2013; Tarasov, 2019). These comparative approaches seek to reduce the impact on inference of over-simplistic models, where multiple character states are unintentionally reduced to one state, because some states are unobserved at the time of analysis ( Beaulieu et al., 2013; Tarasov, 2019). They also seek to resolve epistatic interactions between character states, applicable in cases such as when an evolutionary change resulted in the genomic loss of pathway components in one lineage but not in another ( Maddison, 1993; Tarasov, 2019). A major objective in the development of these approaches has been to create a framework in which developmental insights, such as the descriptions of gene regulatory networks, can be incorporated mathematically into evolutionary comparisons ( Tarasov, 2019).

Establishing null hypotheses of developmental evolution

The key to distinguishing between the scenarios posed above is answering the question of how much evolutionary change we expect for developmental traits. When taxa diverge from a common ancestor, they will retain some shared ancestral characteristics and evolve to become different in others. Simply observing differences between taxa is not in itself surprising ( Jenner, 2006). We suggest that expanding the question from 'Are these traits the same or different between taxa?' to 'Are these traits more or less different between taxa than we would expect?' leads to more interesting and informative results.

Phylogenetic comparative methods answer the latter question by testing findings against a null hypothesis that describes the amount of variation we expect to see by chance ( Gittleman and Kot, 1990), thus serving as a contextual comparison for observed variation. Even when data points are too few for formal modeling and simulation, we believe that the conceptual foundations of this approach are applicable to developmental studies.

A good example is found in developmental comparisons that aim to test the homology of structures or cell types across taxa. In these comparisons, evidence of homology can include similar expression patterns of marker genes, or similar regulatory relationships of homologous genes ( Wagner et al., 2000). In other words, if the development of these structures is regulated by the same genes working together in the same way, this is sometimes considered evidence that they are derived from the same structure in the common ancestor.

Generating a null hypothesis in this case requires considering what we know about the processes that produced the observed data, including four crucial parameters (see Table 1): (1) the time since our studied taxa diverged from a common ancestor; (2) the frequencies at which our structures or cell types of interest arise de novo, and at which they are lost; (3) the frequency of evolutionary change in the expression and function of the genes that regulate structural development; and (4) the uncertainty in determining gene expression or function using laboratory assays.

We find that, for many comparisons of homology, we implicitly assume that all of these parameters are very low. In other words, we assume that the time since divergence has been short enough, the rates of evolution are low enough and the uncertainty in our observations is small enough, such that any observation of shared gene expression is good evidence of homology. Although it may not always be possible to quantify each of these parameters numerically, we should be aware that a default assumption of low parameter values can mislead evolutionary inferences. Instead of this default, we might consider any data we have in hand that suggest this assumption is not valid. If we have reason to believe that the time since divergence is large, rates of structure gain or loss are high, or our assays have a high rate of error, we may conclude that it is very possible to observe convergent expression patterns by chance ( Jenner, 2006; Moczek, 2008). Similarly, if we have reason to believe the divergence time is large or the rate of expression change is high, we might consider it increasingly plausible that we would not be able to detect similar gene expression between taxa, even if the extant trait under study were in fact derived from a common ancestral trait ( Jenner, 2006).

In some cases, parameters 1 and 2 can both be estimated using data from evolutionary studies. For example, we can estimate the time of divergence (parameter 1) using a time-calibrated phylogeny. Data on the presence and absence across of similar structures or cell types are often available from many more taxa than those we study in our laboratories, even if the homology of these structures has not yet been ascertained. This presents an opportunity to evaluate their phylogenetic distribution. For example, if many closely related species differ in the presence of a structure of interest, this suggests that the rate of gain, loss or convergence of the developmental processes that generate that structure is relatively high (parameter 2).

In terms of parameters 3 and 4, developmental biologists are well positioned to provide the data necessary to estimate these parameters. For example, in situ hybridization assays used to assess gene expression rarely yield a single, invariant expression pattern that is identical across all samples. Instead, we are more likely to observe a range of patterns that show some spatial or temporal variation. This likely reflects both the standing variation of gene expression within a population and technical variance in the assay. If the variation due to either one of these is high, the probability of observing any pattern by chance increases. Using these principles to reconsider our assumptions, we can now evaluate comparisons across organisms, not just on the basis of individual experimental outcomes, but against a null hypothesis informed by evolutionary insights and data-driven experience in developmental biology.

Perspectives

Methods for collecting large amounts of developmental data are now more advanced and applicable to a broader range of organisms than ever before, and previously intractable nodes in the evolutionary tree are gaining resolution. We can enhance progress towards the goal of considering development in the light of evolution, by interrogating the assumptions of the evolutionary models we invoke, asking what data we have to support their use, and explicitly incorporating these assumptions and data into our discussions of evolutionary inference. One way to accomplish this is to apply the rich set of models, approaches and conceptual frameworks that have been developed in the field of phylogenetic comparative methods. These concepts and tools can help us establish robust null expectations of developmental evolution that we can use to contextualize our results. By grounding developmental comparisons in evolutionary methods, we can leverage this era of new data to answer some of our oldest questions.

Acknowledgements

We thank Seth Donoughe, Bruno de Medeiros and members of the Extavour Lab for discussion of ideas, and the anonymous reviewers for comments that helped to improve the manuscript.

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# data[].attributes.attachment_link.title
Null hypotheses for developmental evolution

# data[].attributes.attachment_link.description
How much evolutionary change in development do we expect? In this Spotlight, we argue that, as developmental biologists, we are in a prime position to contribute to the definition of a null hypothesis for developmental evolution: in other words, a hypothesis for how much developmental evolution we expect to observe over time.

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A new hypothesis of dinosaur relationships and early dinosaur evolution

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Acknowledgements

We thank S. Chapman (Natural History Museum, London, UK), R. Smith (Iziko South African Museum, Cape Town, South Africa), E. Butler (National Museum, Bloemfontein, South Africa) B. Zipfel (Bernard Price Institute for Palaeontological Research, Johannesburg, South Africa), J. Powell (Instituto Miguel Lillo, Tucumán, Argentina), R. Martinez (Museo de Ciencias Naturales, San Juan, Argentina) and D. Pol (Museo Paleontológico Egidio Feruglio, Trelew, Argentina) for access to specimens in their care, R. Butler, J. Choiniere, B. McPhee, C. VanBuren and K. Chapelle for helpful discussion, M. Williams for assisting with the production of figures, and C. Baron for helpful comments on the manuscript, and the Willi Hennig Society for making TNT 1.5-beta software freely available. Funding for M.G.B. was provided by a NERC/CASE Doctoral Studentship (NE/L501578/1).

Author information

Affiliations

1. Department of Earth Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EQ, UK

Matthew G. Baron & David B. Norman

2. Department of Earth Sciences, Natural History Museum, Cromwell Road, London, SW7 5BD, UK

Matthew G. Baron & Paul M. Barrett

Contributions

M.G.B., P.M.B. and D.B.N. designed this research project. M.G.B., D.B.N. and P.M.B. contributed data. M.G.B. conducted the phylogenetic analyses. M.G.B, D.B.N. and P.M.B. wrote the manuscript. M.G.B. and D.B.N. produced the figures.

Corresponding author

Correspondence to Matthew G. Baron.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks K. Padian, H.-D. Sues and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Ornithoscelida, Ornithischia, Theropoda, Herrerasauridae, Dinosauria and Silesauridae are all very well supported, with Bremer support values of 3 or more. Saurischia (new definition) and Sauropodomorpha are less well supported, with Bremer support values of 2. Bootstrap frequencies below 50 are not shown.

The tree was produced from 79 MPTs (most parsimonious trees) each with a length of 1,627 steps. As in Extended Data Fig. 1, the clades Ornithoscelida and Sauropodomorpha plus Herrerasauridae (Saurischia, new definition) are both recovered. For further details on the additional analyses that were carried out as part of this study, see the Supplementary Information.

The tree was produced from 83 MPTs each with a length of 1,713 steps. For further details on the additional analyses that were carried out as part of this study, see the Supplementary Information.

Tree was produced from 83 MPTs each with a length of 1,690 steps. The clades Ornithoscelida and Saurischia (new definition, see Table 1) are both recovered in this analysis. For further details on the additional analyses that were carried out as part of this study, see the Supplementary Information.

a, Origin of Dinosauria (new definition) when Nyasasaurus is considered. b, Origin of Saurischia (new definition) when Nyasasaurus is considered. c, Origin of Ornithoscelida when Nyasasaurus is considered. For further details on the additional analyses that were carried out as part of this study, see the Supplementary Information.

Supplementary information

This file contains Supplementary Text and Data, Supplementary Tables 1-3 and additional references. (PDF 2569 kb)

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Baron, M., Norman, D. & Barrett, P. A new hypothesis of dinosaur relationships and early dinosaur evolution. Nature 543, 501-506 (2017). https://doi.org/10.1038/nature21700
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# data[].attributes.attachment_link.title
A new hypothesis of dinosaur relationships and early dinosaur evolution

# data[].attributes.attachment_link.description
For 130 years, dinosaurs have been divided into two distinct clades-Ornithischia and Saurischia. Here we present a hypothesis for the phylogenetic relationships of the major dinosaurian groups that challenges the current consensus concerning early dinosaur evolution and highlights problematic aspects of current cladistic definitions.

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Abstract

Sustainability issues have received considerable attention from corporations that embrace corporate sustainability, which focuses on the triple bottom lines of societal, environmental and financial returns or impacts. However, although corporate sustainability has been studied in depth by analyzing qualitative and quantitative data, no studies have involved social media as part of the decision-making process to evaluate a Taiwanese-printed circuit board manufacturing focal firm. This study proposed appropriate attributes and developed the following hybrid method: (i) evaluate the weights of the aspects and attributes as described by qualitative and quantitative data and (ii) analyze how the use of social media modifies their decision-making process. The findings demonstrate that to acquire benefits from sustainable design, code of conduct and eco-products, firms must increase their exchange across corporate functions when dealing with these unexplored sustainability activities. In conclusion, this study compared the results of the above-described data using the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) weighted average method with vague sets and presents the managerial and theoretical implications for operations management studies.

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Using social media and qualitative and quantitative information scales to benchmark corporate sustainability

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Sustainability issues have received considerable attention from corporations that embrace corporate sustainability, which focuses on the triple bottom lines of societal, environmental and financial returns or impacts. However, although corporate sustainability has been studied in depth by analyzing qualitative and quantitative data, no studies have involved social media as part of the decision-making process to evaluate a Taiwanese-printed circuit board manufacturing focal firm.

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A systematic review

Systematic Review of Perioperative and Quality-of-life Outcomes Following Surgical Management of Localised Renal Cancer

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Abstract

Context

For the treatment of localised renal cell carcinoma (RCC), uncertainties remain over the perioperative and quality-of-life (QoL) outcomes for the many different surgical techniques and approaches of nephrectomy. Controversy also remains on whether newer minimally invasive nephron-sparing interventions offer better QoL and perioperative outcomes, and whether adrenalectomy and lymphadenectomy should be performed simultaneously with nephrectomy. These non-oncological outcomes are important because they may have a considerable impact on localised RCC treatment decision making.

Objective

To review systematically all the relevant published literature comparing perioperative and QoL outcomes of surgical management of localised RCC (T1-2N0M0).

Evidence acquisition

Relevant databases including Medline, Embase, and the Cochrane Library were searched up to January 2012. Randomised controlled trials (RCTs) or quasi-randomised controlled trials, prospective observational studies with controls, retrospective matched-pair studies, and comparative studies from well-defined registries/databases were included. The outcome measures were QoL, analgesic requirement, length of hospital stay, time to normal activity level, surgical morbidity and complications, ischaemia time, renal function, blood loss, length of operation, need for blood transfusion, and perioperative mortality. The Cochrane risk of bias tool was used to assess RCTs, and an extended version was used to assess nonrandomised studies (NRSs). The quality of evidence was assessed using Grading of Recommendations, Assessment, Development, and Evaluation.

Evidence synthesis

A total of 4580 abstracts and 380 full-text articles were assessed, and 29 studies met the inclusion criteria (7 RCTs and 22 NRSs). There were high risks of bias and low-quality evidence for studies meeting the inclusion criteria. There is good evidence indicating that partial nephrectomy results in better preservation of renal function and better QoL outcomes than radical nephrectomy regardless of technique or approach. Regarding radical nephrectomy, the laparoscopic approach has better perioperative outcomes than the open approach, and there is no evidence of a difference between the transperitoneal and retroperitoneal approaches. Alternatives to standard laparoscopic radical nephrectomy (LRN) such as hand-assisted, robot-assisted, or single-port techniques appear to have similar perioperative outcomes. There is no good evidence to suggest that minimally invasive procedures such as cryotherapy or radiofrequency ablation have superior perioperative or QoL outcomes to nephrectomy. Regarding concomitant lymphadenectomy during nephrectomy, there were low event rates for complications, and no definitive difference was observed. There was no evidence to base statements about concomitant ipsilateral adrenalectomy during nephrectomy.

Conclusions

Partial nephrectomy results in significantly better preservation of renal function over radical nephrectomy. For tumours where partial nephrectomy is not technically feasible, there is no evidence that alternative procedures or techniques are better than LRN in terms of perioperative or QoL outcomes. In making treatment decisions, perioperative and QoL outcomes should be considered in conjunction with oncological outcomes. Overall, there was a paucity of data regarding QoL outcomes, and when reported, both QoL and perioperative outcomes were inconsistently defined, measured, or reported. The current evidence base has major limitations due to studies of low methodological quality marked by high risks of bias.

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# data[].attributes.attachment_link.title
Systematic Review of Perioperative and Quality-of-life Outcomes Following Surgical Management of Localised Renal Cancer

# data[].attributes.attachment_link.description
For the treatment of localised renal cell carcinoma (RCC), uncertainties remain over the perioperative and quality-of-life (QoL) outcomes for the many different surgical techniques and approaches of nephrectomy. Controversy also remains on whether newer minimally invasive nephron-sparing interventions offer better QoL and perioperative outcomes, and whether adrenalectomy and lymphadenectomy should be performed simultaneously with nephrectomy.

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An article reporting narrative research

Narrative research: Time for a paradigm

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1. Home

2. e-Journals

3. Narrative Inquiry

4. Volume 20, Issue 1

5. Article

ISSN 1387-6740

E-ISSN: 1569-9935

Abstract

As a result of the popularization of the narrative idea and the considerable diversity existing among narrative studies, a rather "all included" conception has arisen, in which the framework of narrative inquiry has been significantly blurred. For narrative inquiry to persist as a unique mode of investigation into human nature, a complementary dialogue is required that aims at outlining its core, alongside the emphasis given in the literature on diversity as its hallmark. As a possible reference point for this debate, recognizing the narrative paradigm that has crystallized since the "narrative turn" is suggested. The narrative paradigm is discussed in light of six major dimensions - ontology, epistemology, methodology, inquiry aim, inquirer posture and participant/narrator posture - indicating that it coincides with other interpretive paradigms in certain aspects yet proffers a unique philosophical infrastructure that gives rise to particular methodological principles and methods. Considering the narrative paradigm as the essence of narrative inquiry asserts that the latter is not confined to a methodology, as often implied. Rather it constitutes a full-fledged research Weltanschauung that intimately connects the "hows" of investigation to the "whats", namely premises about the nature of reality and our relationships with it.

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As a result of the popularization of the narrative idea and the considerable diversity existing among narrative studies, a rather "all included" conception has arisen, in which the framework of narrative inquiry has been significantly blurred.

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An article reporting an experiment or a quasi-experiment

The T2K experiment

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The T2K experiment is a long baseline neutrino oscillation experiment. Its main goal is to measure the last unknown lepton sector mixing angle θ13 by ...

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An article which reports a case study

Case study research in education: A qualitative approach.

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2. An article which describes the aim of the research reported as to ‘explain’

Trying to Explain Home Bias in Equities and Consumption

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Abstract Investors hold a substantially larger proportion of their wealth portfolios in domestic assets than standard portfolio theory would suggest, a phenomenon called "equity home bias." In the absence of this bias, investors would optimally diversify domestic output risk using foreign equities. Therefore, consumption growth rates would tend to co-move across countries even when output growth rates do not. Empirically, however, consumption growth rates tend to have a lower correlation across countries than do output growth rates, a phenomenon I call "consumption home bias." In this paper, I discuss these two biases and their potential relationship as suggested by the literature.

Citation
Lewis, Karen, K. 1999. "Trying to Explain Home Bias in Equities and Consumption." Journal of Economic Literature, DOI: 10.1257/jel.37.2.571

JEL Classification

G11 Portfolio Choice; Investment Decisions

G12 Asset Pricing; Trading volume; Bond Interest Rates

G15 International Financial Markets

G23 Pension Funds; Other Private Financial Institutions; Institutional Investors

E21 Macroeconomics: Consumption; Saving; Wealth

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asset pricing; trading volume; bond interest rates

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pension funds; other private financial institutions; institutional investors

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Trying to Explain Home Bias in Equities and Consumption

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Trying to Explain Home Bias in Equities and Consumption by Karen K. Lewis. Published in volume 37, issue 2, pages 571-608 of Journal of Economic Literature, June 1999, Abstract: Investors hold a substantially larger proportion of their wealth portfolios in domestic assets than standard portfolio the...

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1. An article which describes the aim of the research reported as to ‘explore’

A Secure Base from Which to Explore Close Relationships