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NCCMT - URE - Relative Risk  it’s easy to calculate and interpret

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6 Steps to Formulate a STRONG Hypothesis | Scribbr 🎓

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Quantitative vs. Qualitative Research: The Differences Explained | Scribbr 🎓

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When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Quantitative research
Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish generalizable facts about a topic.
Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias, omitted variable bias, sampling bias, or selection bias.

Qualitative research
Qualitative research is expressed in words. It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.
Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Qualitative research is also at risk for certain research biases including the Hawthorne effect, observer bias, recall bias, and social desirability bias.

The differences between quantitative and qualitative research

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Data collection methods

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies, your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

Surveys: List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).

Experiments: Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.

Observations: Observing subjects in a natural environment where variables can't be controlled.

Qualitative data collection methods

Interviews: Asking open-ended questions verbally to respondents.

Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.

Ethnography: Participating in a community or organization for an extended period of time to closely observe culture and behavior.

Literature review: Survey of published works by other authors.

When to use qualitative vs. quantitative research

A rule of thumb for deciding whether to use qualitative or quantitative data is:

Use quantitative research if you want to confirm or test something (a theory or hypothesis)

Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach. Which type you choose depends on, among other things, whether you're taking an inductive vs. deductive research approach; your research question(s); whether you're doing experimental, correlational, or descriptive research; and practical considerations such as time, money, availability of data, and access to respondents.

Research questionHow satisfied are students with their studies?

Quantitative research approach

You survey 300 students at your university and ask them questions such as: "on a scale from 1-5, how satisfied are your with your professors?"

You can perform statistical analysis on the data and draw conclusions such as: "on average students rated their professors 4.4".

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: "How satisfied are you with your studies?", "What is the most positive aspect of your study program?" and "What can be done to improve the study program?"

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It's also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

How to analyze qualitative and quantitative data

Qualitative or quantitative data by itself can't prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

Other interesting articles

If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about qualitative and quantitative research

What is data collection?

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Cite this Scribbr article

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Streefkerk, R. (2023, June 22). Qualitative vs. Quantitative Research | Differences, Examples & Methods. Scribbr. Retrieved July 12, 2023, from https://www.scribbr.com/methodology/qualitative-quantitative-research/

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Qualitative vs. Quantitative Research | Differences, Examples & Methods

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Quantitative research is expressed in numbers and is used to test hypotheses. Qualitative research is expressed in words to gain understanding.

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Matching Game - Common Clinical Research Terms

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Matching Game - Common Clinical Research Terms

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A research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes.

a person that has a known health problem and participates in research to better understand, diagnose, treat, or cure that disease or condition.

An experimental drug or treatment is administered to a small group of people (20-80) for the first time. The purpose is to evaluate its safety and identify side effects

medical research that involves people to test new treatments and therapies

studies in which the participants do not know which medicine is being used, so they can describe what happens without bias.

a pill or liquid that looks like the new treatment but does not have any treatment value from active ingredients.

The experimental drug or treatment is administered to large groups of people (1,000-3,000) to confirm its effectiveness, monitor side effects, compare it with standard or equivalent treatments.

The experimental drug or treatment is administered to a larger group of people (100-300) to determine its effectiveness and to further evaluate its safety.

a clinical trial that tests the best way to detect certain diseases or health conditions

a clinical trial that provides valuable information about how disease and health progress

After a drug is licensed and approved by the FDA researchers track its safety, seeking more information about its risks, benefits, and optimal use.

a clinical trial that tests new treatments, new combinations of drugs, or new approaches to surgery or radiation therapy.

a carefully designed plan to safeguard the participants' health and answer specific research questions

factors that do not allow participation in a clinical trial or research study

a clinical trial that looks for better ways to prevent a disease in people who have never had the disease or to prevent the disease from returning.

the process by which two or more alternative treatments are assigned to volunteers by chance rather than by choice

a doctor who leads the clinical research team and, along with the other members of the research team, regularly monitors study participants' health to determine the study's safety and effectiveness

process that explains risks and potential benefits about a clinical trial before someone decides whether to participate.

factors that allow someone to participate in a clinical trial or research study

a clinical trial that determines better tests or procedures for diagnosing a particular disease or condition

a clinical trial that explores and measure ways to improve the comfort and quality of life of people with a chronic illness.

a person with no known significant health problems who participates in clinical research to test a new drug, device, or intervention

You did it!
You got every match correct!

Here are all the answers:

Description Match:

medical research that involves people to test new treatments and therapies

Clinical Research

A research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes.

Clinical Trial

a person with no known significant health problems who participates in clinical research to test a new drug, device, or intervention

Healthy Volunteer

factors that allow someone to participate in a clinical trial or research study

Inclusion Criteria

factors that do not allow participation in a clinical trial or research study

Exclusion Criteria

process that explains risks and potential benefits about a clinical trial before someone decides whether to participate.

Informed Consent

a person that has a known health problem and participates in research to better understand, diagnose, treat, or cure that disease or condition.

Patient Volunteer

An experimental drug or treatment is administered to a small group of people (20-80) for the first time. The purpose is to evaluate its safety and identify side effects

Phase I trials

The experimental drug or treatment is administered to a larger group of people (100-300) to determine its effectiveness and to further evaluate its safety.

Phase II trials

The experimental drug or treatment is administered to large groups of people (1,000-3,000) to confirm its effectiveness, monitor side effects, compare it with standard or equivalent treatments.

Phase III trials

After a drug is licensed and approved by the FDA researchers track its safety, seeking more information about its risks, benefits, and optimal use.

Phase IV trials

a pill or liquid that looks like the new treatment but does not have any treatment value from active ingredients.

Placebo

a carefully designed plan to safeguard the participants' health and answer specific research questions

Protocol

a doctor who leads the clinical research team and, along with the other members of the research team, regularly monitors study participants' health to determine the study's safety and effectiveness

Principal Investigator

the process by which two or more alternative treatments are assigned to volunteers by chance rather than by choice

Randomization

studies in which the participants do not know which medicine is being used, so they can describe what happens without bias.

Single- or Double-Blind Studies

a clinical trial that determines better tests or procedures for diagnosing a particular disease or condition

Diagnostic trials

a clinical trial that provides valuable information about how disease and health progress

Natural history studies

a clinical trial that looks for better ways to prevent a disease in people who have never had the disease or to prevent the disease from returning.

Prevention trials

a clinical trial that explores and measure ways to improve the comfort and quality of life of people with a chronic illness.

Quality of life trials

a clinical trial that tests the best way to detect certain diseases or health conditions

Screening trials

a clinical trial that tests new treatments, new combinations of drugs, or new approaches to surgery or radiation therapy.

Treatment trials

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Matching: Common Clinical Research Terms (NIH Definitions) SpeedMatch Review Game

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A research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes. a person that has a known health problem and participates in research to better understand, diagnose, treat, or cure that disease or condition.

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In case you missed it.

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Video Conferencing, Web Conferencing, Webinars, Screen Sharing

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Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done.

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Case Control Study Example: The INTERHEART Study

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Case Control Study Example: The INTERHEART Study

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Secondhand Smoke and Myocardial Infarction

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Your access to the NCBI website at www.ncbi.nlm.nih.gov has been temporarily blocked due to a possible misuse/abuse situation involving your site. This is not an indication of a security issue such as a virus or attack. It could be something as simple as a run away script or learning how to better use E-utilities, http://www.ncbi.nlm.nih.gov/books/NBK25497/, for more efficient work such that your work does not impact the ability of other researchers to also use our site. To restore access and understand how to better interact with our site to avoid this in the future, please have your system administrator contact info@ncbi.nlm.nih.gov.

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Ecological Study: Effect of Democracy on Health

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Ecological Study: Effect of Democracy on Health

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Case Series: Pneumocystis Pneumonia --- Los Angeles

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Case Series: Pneumocystis Pneumonia --- Los Angeles

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Persons using assistive technology might not be able to fully access information in this file. For assistance, please send e-mail to: mmwrq@cdc.gov. Type 508 Accommodation and the title of the report in the subject line of e-mail.

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Evolution of Clinical Research: A History Before and Beyond James Lind

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Evolution of Clinical Research: A History Before and Beyond James Lind

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Meta-Analysis - A meta-analysis of the efficacy of HAART on HIV transmission and its impact on sexual risk behaviours among men who have sex with men

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Meta-Analysis - A meta-analysis of the efficacy of HAART on HIV transmission and its impact on sexual risk behaviours among men who have sex with men

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Screening and inclusion criteria

The following criteria were carried out for literature research:

1. 1.
Participants were over 18 years old (including 18) or defined as adults according to National Laws.

2. 2.
Participants were homosexual males, self-identified as gay or engaging in male-to-male sexual behaviours regardless of original sexual orientation.

3. 3.
HAART were exposed on HIV positive rather than negative participants.

4. 4.
Studies performed to examine 1) the risk of HIV transmission, and/or 2) any types of UAI ( i.e. receptive or insertive).

5. 5.
Studies published as journal articles in English with peer reviewed.

6. 6.
Full text articles are available online.

7. 7.
Articles published between January 1996 to February 2017.

Search strategies and literature research

Electronic database PubMed, ScienceDirect and Google Scholar were fully searched. Terms'HIV', 'antiretroviral', 'transmission', 'men who have sex with men', 'homosexual', 'behaviours', 'treatment as prevention', 'test and treat', 'anal sex' were crossly combined and searched as either keywords in titles and abstracts or Medical Subject Heading (MeSH) terms ( i.e. 'HIV' and 'behaviour'). The literature research was conducted mainly in the form of two search strings: (1) 'HIV', 'antiretroviral', 'transmission', ('men who have sex with men' OR 'homosexual'); (2) 'HIV', 'antiretroviral', ('men who have sex with men' OR 'homosexual'), 'behaviours'. Keywords 'test and treat', 'treatment as prevention' 'anal sex' were performed as supplementary search strategies with the other terms. Keywords 'pre-exposure', 'hepatitis', 'HPV', 'herpes', 'drug resistance' and 'prevalence' were excluded from title and abstract while searching at the database of Google Scholar. The search period was adopted from January 1996 to the present (February 2017).

Screening

2327 articles were found and 1616 articles were remained for further examination after removing duplications. Titles or abstracts were excluded if relating to:

1. (1)
irrelative study purposes: psychological and mental health,virology, health economics and policy, other STIs and AIDS-related diseases, drug resistance, belief or attitudes towards safe sex rather than behavioural changes, characters of participant, HAART scaling-up strategies.

2. (2)
irrelative exposures (HAART) or study groups: PrEP or PEP, alternative interventions ( i.e. behaviours, partner notification, testing and counselling),heterosexual or women participants only.

3. (3)
irrelative study design: molecular epidemiology, qualitative researches, grey articles, news and reviews.

As a result, 75 articles were left for eligibility assessment via full text reviewed. The following principles were used to guide the full text article screening of this meta-analysis:

1. 1.
Studies performed to examine the efficacy of HAART on HIV transmission and/or the relationship of HAART and the UAI irrespective types of sexual patterns and partners.

2. 2.
Studies were focused on the change of actual sexual behaviours rather than the change of attitudes towards sexual behaviours.

3. 3.
If participants were made up of mixed subgroups ( i.e. homosexual, heterosexual and bisexual), the subgroup of homosexual males or male couples would be included in this meta-analysis.

4. 4.
If the database overlapped between articles, with similar research purposes, the one contained the widest range of data would be included.

5. 5.
Outcomes were measured for HIV incidence or the likelihood of engaging UAI.

6. 6.
The ecological studies would not be included, because exposures were multiple and aggregated, potential confounders and the ecological fallacy may contribute great heterogeneity to the pooled analysis.

57 articles were excluded via full text screening with following reasons listed in the Fig. 4. Finally, eighteen articles were included in this quantitative analysis. However, data presented in three articles were not eligible for meta-analyses. Two studies measured by the incidence per year, which was not comparable with other studies. The other study measured the association between HAART and HIV transmission risk by rate ratio (RR) was hardly fitted into the third meta-analysis. Thus, there were fifteen full text articles enrolled in the final data analysis. This systematic review was carried out according to guidelines by PRISMA checklist. The PRISMA flaw chat for the data selection procedure has been followed and presented in Fig. 4.

Data extraction

Each study was numbered by the first author and published year. A unified form was designed for the data extraction and imported into a Microsoft Excel database, including following categories:

1. (1)
Study design characters: authors and years, location, study design, study setting, data collection methods, study period, sample size for analysis, recall period (within past months), response rate, length of follow-up (person-year);

2. (2)
Participants' characters: ethnicity, age, sero-status of participants, education, income, heavy alcohol users, substance use, number of sexual partners during recall period, since when diagnosed, type of data analysis;

3. (3)
Findings: measurement of effect size and findings.

If both adjusted and unadjusted effect sizes were reported by independent studies, the unadjusted one would be included for the meta-analysis. Additionally, if the effect size was stratified by regions or types of sexual patterns, the sub-level number would be added into the meta-analysis. The extracted items listed in standard forms (Tables 1, 2, 3 and 5) were tested by three different pilot studies.

Data analysis

Three independent meta-analysis were performed. The pooled risk of HIV transmission was measured separately by the IR (the number of new infections divided by at risk population over specific time) and the PCR (estimation based on Bernoulli model or boost rapping algorithm likelihood function) with 95% CI. The pooled OR with 95% CI was used to estimate the association between HAART and UAI, representing the probability of engaging UAI between the on-HAART group (index group) and the non-HAART group (reference group). The baseline data in cohort studies were included in meta-analysis. If OR is over 1, it represented that the index group is more likely to engage in UAI than the reference group. Otherwise, the index group is less likely to engage in UAI (OR < 1). If OR = 1, it means there is no association between both variables.

A Cochran's Q test based on Chi-square test and an I ² test were used to examine the heterogeneity across independent studies. The fixed-effects model would be used if the data showed low or moderate heterogeneity (p > 0.05, I ² < 50%), otherwise, the random-effects model (p < 0.05, I ²  > = 50%) would be carried out. Publication bias was tested by the Egger's regression intercept, if p > 0.05, it means no evidence on publication bias, otherwise, there would be a publication bias existing. Statistical significance was 0.05 (P < 0.05) with 95% CI. Data were pooled and analysed by the software Comprehensive Meta-Analysis (version 2.0, Biostat, Englewood, New Jersey).

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A meta-analysis of the efficacy of HAART on HIV transmission and its impact on sexual risk behaviours among men who have sex with men

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Evidence showed preventive impacts of the highly active antiretroviral therapy (HAART) on the Human Immunodeficiency Virus (HIV) transmission amomg heterosexual population, however, that is of deficit among men who have sex with men (MSM). The aim was to systematically examine the efficacy of HAART on HIV transmission and the association between the HAART initiation and unprotected anal intercourse (UAI) in MSM population.

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Systematic Review: A Systematic Review of Interventions to Improve Diabetes Care in Socially Disadvantaged Populations

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Systematic Review: A Systematic Review of Interventions to Improve Diabetes Care in Socially Disadvantaged Populations

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Abstract

OBJECTIVE -To identify and synthesize evidence about the effectiveness of patient, provider, and health system interventions to improve diabetes care among socially disadvantaged populations.

RESEARCH DESIGN AND METHODS -Studies that were included targeted interventions toward socially disadvantaged adults with type 1 or type 2 diabetes; were conducted in industrialized countries; were measured outcomes of self-management, provider management, or clinical outcomes; and were randomized controlled trials, controlled trials, or before-and-after studies with a contemporaneous control group. Seven databases were searched for articles published in any language between January 1986 and December 2004. Twenty-six intervention features were identified and analyzed in terms of their association with successful or unsuccessful interventions.

RESULTS -Eleven of 17 studies that met inclusion criteria had positive results. Features that appeared to have the most consistent positive effects included cultural tailoring of the intervention, community educators or lay people leading the intervention, one-on-one interventions with individualized assessment and reassessment, incorporating treatment algorithms, focusing on behavior-related tasks, providing feedback, and high-intensity interventions (>10 contact times) delivered over a long duration (≥6 months). Interventions that were consistently associated with the largest negative outcomes included those that used mainly didactic teaching or that focused only on diabetes knowledge.

CONCLUSIONS -This systematic review provides evidence for the effectiveness of interventions to improve diabetes care among socially disadvantaged populations and identifies key intervention features that may predict success. These types of interventions would require additional resources for needs assessment, leader training, community and family outreach, and follow-up.

Diabetes affects at least 171 million people worldwide and is dramatically increasing in many countries as a consequence of increased obesity, sedentary lifestyle, and aging populations ( 1). Certain population subgroups are at higher risk of acquiring diabetes including nonwhite ethno-racial groups and those with low socioeconomic status (SES) ( 2). These same groups are often at higher risk of late diagnosis, poor diabetes control and self-management, the development of diabetes-associated complications, and more frequent emergency room visits and hospitalizations ( 3- 8).

Interventions to improve diabetes outcomes can be directed at individuals with diabetes, health providers, or the health system. Patient-level interventions include those directed at improved self-management, including medication taking, diet, exercise, self-monitoring, and appropriate use of health care services ( 9). Continuing professional education and knowledge translation activities are examples of strategies intended to improve health provider care ( 10, 11, 12, 13). Health system changes with the potential to improve access to care and quality of care include expanded hours of service, language translation, case management, reducing financial barriers to health providers and medications, and changes in health care provider roles ( 14, 15).

When interventions are designed for general populations, however, disadvantaged groups may not be reached or may not be able to take full advantage of the opportunities provided to them. Socially disadvantaged groups may experience difficulties with language, cultural beliefs, transportation, getting time off work, child care, and low health literacy in addition to financial barriers to care. Given that these factors can influence uptake, interventions designed to reduce health inequalities may need to be targeted to specific audiences and place special emphasis on the way programs are designed and delivered to promote access to and use of resources and services ( 16).

In this systematic review, we define socially disadvantaged groups as those that have low SES or belong to an ethno-racial minority. We conceptualize social disadvantage as related to patient, provider, and health system factors that can affect self-management and provider management and ultimately manifest as clinical outcomes. The objective of this systematic review is to identify and synthesize evidence about the effectiveness of patient, provider, and health system interventions to improve diabetes care among socially disadvantaged populations.

RESEARCH DESIGN AND METHODS

Inclusion criteria

We included studies that targeted interventions toward socially disadvantaged adults with type 1 or type 2 diabetes and that were conducted in industrialized countries and measured self-management, provider management, or clinical outcomes. Self-management includes home glucose monitoring, regulation of diet and exercise, adherence to and self-adjustment of medications, and attendance at office appointments when needed or as scheduled by the provider. Provider management includes diagnostic testing, prescribing, educational and behavioral counseling, and referrals. Clinical outcomes include measures of fasting blood glucose, glycosylated hemoglobin (A1C), blood pressure, BMI and lipids, and patient-oriented outcomes of hospitalization, mortality, diabetes complications, and quality of life. To be included, studies had to be randomized controlled trials, controlled trials, or before-and-after studies with a contemporaneous control group. Articles written in all languages were included. Studies were excluded if they focused on specific age-groups such as youth, targeted only gestational diabetes, included only hospital process-of-care measures, or did not clearly specify the socially disadvantaged group being studied.

Search strategy

In consultation with an information specialist, electronic databases, including Medline, Embase, Cinahl, HealthStar, Cochrane Library, Sociological Abstracts, Social Science Citation Index, and International Pharmaceutical Abstracts, were searched for relevant studies from articles published in any language between January 1986 and December 2004. The references of review articles and of included original publications were also screened for potentially relevant studies.

Methods of the review

Two reviewers (N.R.K. and K.W.) independently screened citations and abstracts to identify articles potentially meeting the inclusion criteria. For those articles, full text versions were retrieved and independently screened by two reviewers to determine whether they met inclusion criteria. Disagreements about whether the inclusion criteria were met were resolved through discussion with a third reviewer (J.B. or R.H.G.).

Data extraction

Data extraction of relevant study information for articles meeting inclusion criteria was performed independently by two reviewers (N.R.K. and K.W.). Disagreements were resolved through discussion. A data extraction form was used to collect information on type of social disadvantage (low SES population or specific ethno-racial group), study objectives, study design, setting of intervention, study population (including demographics, SES, and health insurance), measures and scales used, and main findings.

To systematically describe the scope and components of the interventions, specific features were extracted from each intervention article using main components that have been previously described for the taxonomy of diabetes interventions by Elasy et al. ( 17). We extracted the main target of the intervention (patient, provider, or health system); type of needs assessment conducted, tailoring to the population; components of the intervention; diabetes-related content; timing/intensity; and the provider of the intervention. For articles that referenced previous publications (e.g., describing needs assessments or interventions), the original publication was retrieved and relevant data summarized.

Validity assessment

To assess the methodological quality of the intervention studies, we adapted the criteria suggested by Jadad et al. ( 18) if the study was described as randomized, if the sequence of randomization was described and appropriate, and if there was a description of withdrawals and dropouts. We did not assess blinding, as the types of interventions under study could not be blinded. We also assessed whether there was an adequate description of the intervention.

Data analysis

Due to heterogeneity of populations, interventions, and outcomes, it was not possible to combine outcomes in a meta-analysis. As an alternative, we used a recently described method to identify specific features of interventions that are likely to be associated with successful or unsuccessful outcomes ( 19). A total of 26 features were identified based on five categories: 1) target of the intervention, 2) how the intervention was designed, 3) setting of the intervention, 4) how the intervention was delivered, and 5) intensity and duration of the intervention (listed in Table 3). For each intervention, two reviewers (J.B. and N.R.K.) independently determined the absence or presence of these specific features. Disagreements were resolved through discussion with a third reviewer (R.H.G.).

Next, two reviewers (R.H.G. and N.R.K.) independently assessed whether the intervention resulted in a positive outcome that was both statistically and clinically significant. Statistical significance was set at P < 0.05, but due to the large number of possible outcomes, no predefined criteria for clinical significance were established. Instead, each reviewer provided a global judgment about clinical importance. In circumstances where studies reported multiple types of outcomes, judgments were primarily based on clinical measures. For studies that did not report clinical measures, provider management and self-management outcomes were used to make judgments. For each of the 26 features, we calculated the percentage success rate with and without the feature (percent of articles with the feature having positive outcomes minus percent of articles without the feature having positive outcomes). Positive and negative rate differences indicated that interventions with and without the feature, respectively, were more often associated with positive study results. Due to the small number of articles included, CIs were not calculated and we limited judgments about features to those that appeared in at least five included studies. Our use of this analytic approach was therefore more descriptive than quantitative. To assess whether our findings were related to the methodological quality of the included studies, two sensitivity analyses were performed as follows. 1) Studies were limited to those with a randomized controlled design, and 2) studies with randomized controlled designs were further limited to those in which randomization was described and appropriate and which had a description of withdrawals.

RESULTS

Description of studies

The initial search identified a total of 16,750 citations. After scanning titles of the citations, 615 were accepted for further screening and complete abstracts of these studies were reviewed. Of these, 163 citations were identified as potentially meeting the inclusion criteria. In addition, five articles were identified when scanning bibliographies of relevant published articles in the field. After examination of full text articles, 17 intervention studies ( 20- 36) were included in the review. Common reasons for exclusion were that the population under study did not meet the criteria for a socially disadvantaged population and/or there was no comparison group involved.

Methodological quality

Ten ( 21, 22, 25, 28- 30, 32- 34) of 17 studies were described as randomized trials. The randomization process was described and appropriate in 6 ( 22, 25, 28, 30, 32, 33) of 10 studies, all of which also had a description of withdrawals and dropouts and all had a well-described intervention.

Description of features of the interventions

Six ( 28, 29, 30, 33, 34, 36) of 17 studies provided diabetes education as the main focus of their intervention. Educational interventions used a variety of delivery methods including didactic lectures, skill building, problem solving, behavioral strategies (e.g., exercise), feedback (e.g., diaries), family member participation, and/or individualized assessment.

Six ( 20- 23, 25, 31) of 17 studies delivered an intervention that focused on innovative health care provider roles. These included individualized assessment using treatment algorithms by nurse case managers and clinical pharmacists and coordination of care by community health workers.

Other studies described interventions that were more focused on specific aspects of diabetes-related care. These included physical activity sessions ( 35), a focused education and support intervention to improve ophthalmic examinations ( 32), implementation of a visual communication tool ( 27), reminder cards for diabetes care indicators ( 24), and rapid A1C testing ( 26).

Analysis of features

We identified 26 features of the diabetes interventions ( Table 3). Eight of these features appeared in fewer than five studies each, limiting any judgment about their effectiveness. Among the remaining 18 features, 13 were associated with positive rate differences and thus were more likely to be associated with successful interventions in socially disadvantaged populations. Features found in multiple studies with more consistent effects included cultural tailoring of the intervention, use of community educators or lay people leading the intervention, one-on-one interventions with individualized assessment and reassessment, use of treatment algorithms by various health care providers, focus on behavior-related tasks in the intervention, use of feedback about patient's control of disease or performance, and high-intensity interventions (>10 contact times) delivered over a long duration (≥6 months). Interventions in multiple studies that were associated with the largest negative rate differences included those that used mainly didactic teaching or that focused only on diabetes knowledge.

Sensitivity analyses limiting results to only higher methodological quality studies indicated that most of the features had similar rate differences (remained positive or negative in the analysis), except that use of feedback to patients and treatment algorithms had an inconsistent pattern.

CONCLUSIONS

The results of this systematic review provide reason for optimism that interventions for socially disadvantaged populations can be effective and have the potential to reduce health disparities in diabetes care and outcomes. They also have practice and policy implications. The results indicate that short-term group-based didactic teaching aimed at improving diabetes knowledge may be of limited value for disadvantaged populations. To be successful, interventions appear to require organizational inputs and resources that go well beyond traditional diabetes education programs. The time and effort required to adapt interventions to the local community is likely to yield important benefits but must be done in each setting to fit local circumstances. The key aspects of tailoring in the reviewed studies were for culture and health literacy, both of which require substantial expertise and knowledge of local communities. Achieving a high degree of responsiveness to individual needs and making extensive use of behavioral methods requires one-on-one attention by highly trained personnel, again, with significant resource implications. If those personnel are community members, additional time and effort is required for their training, supervision, and support. Finally, successful interventions may require multiple contacts over a prolonged period of time.

Are such interventions affordable? Many of the resources needed for such community-oriented behaviorally mediated and intensive interventions are "up front," including intervention design and recruiting and training community-based educators. Ongoing costs will mostly relate to multiple one-on-one sessions. The disadvantaged populations included in this study have significantly higher rates of diabetes complications, health services use, and poor control. A 1% drop in A1C has been associated with a 10% reduction in diabetes-related deaths and a 25% reduction in microvascular end points ( 39). Interventions targeting glucose, blood pressure, and lipid control simultaneously have the potential for up to 50% reductions in cardiovascular events and risk of microvascular complications ( 40). Reductions of 1% in A1C were achieved in several of the successful interventions included in this review, as were substantial improvements in blood pressure and lipids, so it is reasonable to expect major impacts on morbidity, mortality, and health services use from interventions such as these.

Our findings were less clear in several areas. Due to the small number of studies included and inconclusive results, it was not possible to clearly identify whether patient, provider, system-level interventions, or combinations of these are most effective. The results also did not point to hospital, primary care, or community as the best setting for intervention. Other features appeared to have positive effects, but definitive conclusions could not be made due to low numbers of studies identified. These features require further study and included needs assessment to design the intervention, delivery of comprehensive self-management education, problem solving tasks as part of the intervention, and the incorporation of family support.

The features of successful interventions identified in our review are consistent with those described for multiple health conditions as leading to positive outcomes for disadvantaged groups ( 16). Arblaster et al. ( 16) found that to reduce health inequalities among disadvantaged groups, intensive approaches, community involvement, face-to-face interventions, development of skills to promote behavior change, attention to the agent delivering the intervention, provision of prompts and reminders, and the use of prior needs assessment to inform intervention design were needed. They also found that multifaceted interventions and multidisciplinary approaches were desirable.

A review that was limited to diabetes self-management interventions in disadvantaged populations included five of the studies in this systematic review ( 41). That study found that interventions in community settings and those that explicitly address social-contextual issues tended to be more effective. They also identified the need to intervene at multiple levels with multiple strategies. Changing behavior and diabetes control in the long term may be much more difficult than achieving increases in knowledge. Active, hands-on, participatory, and behavioral approaches in self-management education appear to be more effective than didactic interventions, but long-term glycemic control tends to decline once interventions are over ( 9, 42).

This study has several limitations. Social disadvantage is defined differently across studies, which limits their comparability ( 16, 41). The studies included in this review primarily focused on black, Hispanic, and low-income people living in the U.S., and it is not known how these results relate to other socially disadvantaged populations. Our findings are based on a small number of heterogeneous studies that could not be combined in meta-analysis due to varying populations, interventions, and outcomes. The small number of studies and large heterogeneity also means that our conclusions about the effectiveness of various intervention strategies should be interpreted with caution. Few studies included in this review reported outcomes beyond 1 year, so the long-term effectiveness of interventions for disadvantaged populations is not known. Many of the included interventions had multiple components, and it was rarely possible to isolate the key elements that were responsible for beneficial outcomes. Additionally, no studies measured relevant patient-oriented outcomes. These issues represent areas for future research.

In conclusion, this systematic review highlights important features of interventions to improve diabetes care among socially disadvantaged populations. Key strategies include cultural tailoring of the intervention, community educators or lay people leading the intervention, one-on-one interventions with individualized assessment and reassessment, incorporating treatment algorithms, focusing on behavior-related tasks, providing feedback, and high-intensity interventions (>10 contact times) delivered over a long duration (≥6 months). These types of interventions would require additional resources for needs assessment, leader training, community outreach, and follow-up. Active and intensive strategies are justified, however, by the large burden of illness born by disadvantaged groups with diabetes, their greater need for health care services, and the demonstrated efficacy of diabetes control in reducing adverse outcomes. Further research is needed, in particular on which strategies are most effective for specific groups, on which interventions produce long-term improvement in clinical and patient-oriented diabetes outcomes, on interventions aimed at providers and the health system, and that incorporate a formal economic analysis.

Acknowledgments

This work is supported by the Ontario Ministry of Health and Long-Term Care and an unrestricted grant from BMO Financial Group.

The opinions, results, and conclusions are those of the authors, and no endorsement by the Ministry is intended or should be inferred. Rebecca Zakoor, Manager, St. Michael's Hospital Health Sciences Library, generously assisted with development and implementation of electronic search strategy.

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A Systematic Review of Interventions to Improve Diabetes Care in Socially Disadvantaged Populations

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OBJECTIVE -To identify and synthesize evidence about the effectiveness of patient, provider, and health system interventions to improve diabetes care among socially disadvantaged populations. RESEARCH DESIGN AND METHODS -Studies that were included targeted interventions toward socially disadvantaged adults with type 1 or type 2 diabetes; were conducted in industrialized countries; were measured outcomes of self-management, provider management, or clinical outcomes; and were randomized controlled trials, controlled trials, or before-and-after studies with a contemporaneous control group.

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(Thumbnail not working) Contains great infographic with different study designs arranged by strength of Evidence

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Skip to main content

What are the types of reviews?

As you begin searching through the literature for evidence, you will come across different types of publications. Below are examples of the most common types and explanations of what they are. Although systematic reviews and meta-analysis are considered the highest quality of evidence, not every topic will have an SR or MA.

Literature Review Examples

Remember, a literature review provides an overview of a topic. There may or may not be a method for how studies are collected or interpreted. Lit reviews aren't always obviously labeled "literature review"; they may be embedded within sections such as the introduction or background. You can figure this out by reading the article.

Systematic Review Examples

Systematic reviews address a clinical question. Reviews are gathered using a specific, defined set of criteria.

Tips:

Selection criteria is defined

The words "Systematic Review" may appear int he title or abstract

Use Cochrane Library to search for reviews
BTW -> Cochrane Reviews aka Systematic Reviews

Meta-Analysis Examples

Meta-analysis is a study that combines data from OTHER studies. All the studies are combined to argue whether a clinical intervention is statistically significant by combining the results from the other studies. For example, you want to examine a specific headache intervention without running a clinical trial. You can look at other articles that discuss your clinical intervention, combine all the participants from those articles, and run a statistical analysis to test if your results are significant. Guess what? There's a lot of math.

Tips:

Include the words "meta-analysis" or "meta analysis" in your keywords

Meta-analyses will always be accompanied by a systematic review, but a systematic review may not have a meta-analysis

See if the abstract or results section mention a meta-analysis

Use databases like Cochrane or PubMed

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LibGuides: EBP Resources for West Florida Hospital: Types of EBP Literature

# data[].attributes.attachment_link.description
Skip to main content As you begin searching through the literature for evidence, you will come across different types of publications. Below are examples of the most common types and explanations of what they are. Although systematic reviews and meta-analysis are considered the highest quality of evidence, not every topic will have an SR or MA.

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Three Ways for Scientists to Communicate Their Results of Scientific Research

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Three Ways for Scientists to Communicate Their Results of Scientific Research

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The usefulness of scientific knowledge is limited if that knowledge is not communicated to other people. Scientists often communicate their research results in three general ways. One is to publish their results in peer-reviewed journals that can be ready by other scientists. Two is to present their results at national and international conferences where other scientists can listen to presentations. Scientists also present their results to certain departments at universities. Third, scientists publish about their work in popular media, such as magazines, newspapers, and blogs.

The main ways that scientists communicate research results is by publishing the results in journals. Journals are archived and can be read by other people in the future. Some journals are peer-reviewed, meaning they only publish articles that pass a certain standard of quality -- peer-reviewed journals are usually for a specific audience, such as other scientists. Publications give scientists the most long-lasting and widespread audience. A recent movement in journal publishing is called open-access. Open-access journals no longer charge readers with subscription fees, meaning anyone with Internet access can read these journals.

The second most common way for scientists to communicate their research results is to present the results at conferences. Conferences can range from several dozen attendees to tens of thousands of attendees. Conferences are places where scientists not only share their latest research findings, but also network with other scientists for the purposes of collaboration, or teamwork. They are also places where scientists share about research enigmas and get advice from each other about how to solve those problems. Conferences bring together scientists of all ages, allowing the younger scientists to connect with older, more established scientists.

Research conferences vary in how often then occur, which can be once every few years to every few months. However, every weekday is an opportunity for scientists to be invited to present their research to university departments. University departments usually have many weekly seminars, in which scientists from universities, research institutions and companies are invited to speak. Each department of a university specializes in a specific discipline, which provides a smaller and more well-informed audience on the topic that is presented by the speaker.

Scientists not only want to inform their colleagues about their latest results, but may also want to communicate new data to the public. Popular media outlets are read by more people than peer-reviewed journals, and provide a wider audience. Magazines, such as Scientific American, and National Geographic; newspapers, such as The New York Times; and television stations, such as CNN, provide much more exposure than a peer-reviewed journal. Scientists now also publish about their work on blog sites.

About the Author

David H. Nguyen holds a PhD and is a cancer biologist and science writer. His specialty is tumor biology. He also has a strong interest in the deep intersections between social injustice and cancer health disparities, which particularly affect ethnic minorities and enslaved peoples. He is author of the Kindle eBook "Tips of Surviving Graduate & Professional School."

Photo Credits

Comstock/Stockbyte/Getty Images

# data[].attributes.attachment_link.title
Three Ways for Scientists to Communicate Their Results of Scientific Research

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The usefulness of scientific knowledge is limited if that knowledge is not communicated to other people. Scientists often communicate their research results in three general ways. One is to publish their results in peer-reviewed journals that can be ready by other scientists.

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Video Conferencing, Web Conferencing, Webinars, Screen Sharing

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Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done.

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Video Conferencing, Web Conferencing, Webinars, Screen Sharing

# data[].attributes.attachment_link.description
Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done.

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This is very useful for understanding what is in each section, whether reading or writing an article

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Knowing how to interpret an odds ratio (OR) allows you to quickly understand whether a public health intervention works and how big an effect it has. For example, how effective is the flu vaccine in preventing people from getting the flu?

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Statistics made easy ! ! !   Learn about the t-test, the chi square test, the p value and more

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What makes a well-controlled clinical trial

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What makes a well-controlled clinical trial

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John Lehmann, Director of Business Development, IMARC
A well-controlled clinical trial is much like a well-controlled experiment. They are both rooted in the cornerstone of the scientific method. What exactly is a clinical trial? According to clinicaltrials.gov , "In a clinical trial (also called an interventional study), participants receive specific interventions according to the research plan or protocol created by the investigators. These interventions may be medical products, such as drugs or devices; procedures; or changes to participants' behavior, for example, diet." In the

In the 21 CFR 314.126 , the FDA lays out regulations as to what makes an "adequate and well-controlled study" as one that has:

A clear statement of the objectives of the investigation and a summary of the proposed methods of analysis in the protocol.

The study uses a design that permits a valid comparison with a control to provide a quantitative assessment of the effect.

The method of subject selection that provides adequate assurance that the subject has the disease or condition that the treatment is directed at.

The method of assigning patients to treatment and control groups minimizes bias and assures comparability of the groups.

Adequate measures are taken to minimize bias, by the subjects, observers and the data analysts.

The methods of assessment of subjects' response are well-defined and reliable.

There is an analysis of the results of the study that is adequate to assess the effect of the drug or device.

A well-controlled experiment asks a question or a hypothesis. This hypothesis is then tested by the experiment. What characterizes a well-controlled experiment?

A well-designed experiment is simple and easily replicated. It maintains strict control on all variables.

The variables in the experiment are designed to prove or disprove a casual relationship between the independent variable and the dependent variable. This would be the drug or device versus the condition of the patient.

Control is a vital element of a well-designed experiment of the main variables. There needs to be a way to rule out the effects of extraneous variables other than the dependent and independent ones.

A good experiment, like a good clinical trial, often has blind controls or double-blind randomization to compare the end results. The goal for a well-controlled experiment is for it to be repeated many times with the same or statistically similar results. Clinical trials are typically not repeated as much as they are designed with large numbers of subjects to remove the bias that could occur in a study with small subject sampling. This helps to rule out random samples or outliers in the "experiment".

Many would argue there are other things that go into the makeup of a well-controlled clinical trial, especially once sites and patients get involved. Having a research team with adequate facilities, knowledge of the federal regulations, the time and staff to work on the project is imperative. Additionally, having timely, well-documented data is vital to the trials continued success, which helps ensure the trial is being conducted in a way that will produce relevant results.

What are your thoughts on what makes a well-controlled trial?
The opinions expressed in this blog post are the author's only and do not necessarily reflect those of MassDevice.com or its employees.

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What makes a well-controlled clinical trial?

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John Lehmann, Director of Business Development, IMARC A well-controlled clinical trial is much like a well-controlled experiment. They are both rooted in the cornerstone of the scientific method. What exactly is a clinical trial? According to clinicaltrials.gov, "In a clinical trial (also called an interventional study), participants receive specific interventions according to the research plan [...]

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Common Sources of Bias in Health Research

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Common Sources of Bias in Health Research

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One of the main problems with scientific studies is that bias (the conscious or unconscious influencing of the study and its results) can make them less dependable. bias can occur in a number of different ways and it is important for researchers to be aware of these and find ways to minimize bias. There are a great number of ways that bias can occur, these are a few common examples:
Recall bias
When survey respondents are asked to answer questions about things that happened to them in the past, the researchers have to rely on the respondents' memories of the past. Sometimes different types of events are more likely to be remembered than others, causing respondents to report those types of experiences more readily. This creates a form of bias called recall bias.
Selection bias
Research samples can sometimes under-represent certain people or groups, and over-represent others. This is called sample selection bias. The best way to select people for research is using the basis of chance, in other words, so that everyone in the population being investigated has an equal chance of being selected. This is called randomisation, because people are randomly selected to take part in the study.
Observation bias (also known as the Hawthorne Effect)
Observation bias occurs when participants in a study are aware that they are being observed by scientists and, either consciously or unconsciously, alter the way they act or the answers they give.
Confirmation bias
Confirmation bias is a type of bias that may occur during the interpretation of study data when researchers, consciously or unconsciously, look for information or patterns in their data that confirm the ideas or opinions that they already hold.
Publishing bias
Studies with negative findings (i.e. trials in which no significant results are found) are less likely to be submitted by scientists or published by scientific journals because they are perceived as less interesting. These 'negative' results are as important for understanding a scientific topic as significant results are but they are less likely to be published. This can skew our understanding of a topic because, for example, when carrying out a review or a meta-analysis on a new drug treatment, if this type of data is missing, it can make it seem like a drug is more or less effective than it actually is. This is called publishing bias.

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One of the main problems with scientific studies is that bias (the conscious or unconscious influencing of the study and its results) can make them less dependable. bias can occur in a number of different ways and it is important for researchers to be aware of these and find ways to minimize bias.

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Translating Research Evidence into Practice to Reduce Health Disparities: A Social Determinants Approach

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Translating Research Evidence into Practice to Reduce Health Disparities: A Social Determinants Approach

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Your access to the NCBI website at www.ncbi.nlm.nih.gov has been temporarily blocked due to a possible misuse/abuse situation involving your site. This is not an indication of a security issue such as a virus or attack. It could be something as simple as a run away script or learning how to better use E-utilities, http://www.ncbi.nlm.nih.gov/books/NBK25497/, for more efficient work such that your work does not impact the ability of other researchers to also use our site. To restore access and understand how to better interact with our site to avoid this in the future, please have your system administrator contact info@ncbi.nlm.nih.gov.

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Your access to the NCBI website at www.ncbi.nlm.nih.gov has been temporarily blocked due to a possible misuse/abuse situation involving your site. This is not an indication of a security issue such as a virus or attack.

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How statistics can be misleading - Mark Liddell

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Sir,

Scientific research is based upon finding a solution to a particular problem one can identify. There are various methods of formulating a research design for the study. Two broad approaches of data collection and interpretation in research are qualitative and quantitative research. The elementary method of conducting research was quantitative, but recently, qualitative method of research has also gained momentum among researchers.

Qualitative research focuses in understanding a research query as a humanistic or idealistic approach. Though quantitative approach is a more reliable method as it is based upon numeric and methods that can be made objectively and propagated by other researchers. Qualitative method is used to understand people's beliefs, experiences, attitudes, behavior, and interactions. It generates non-numerical data. The integration of qualitative research into intervention studies is a research strategy that is gaining increased attention across disciplines. Although once viewed as philosophically incongruent with experimental research, qualitative research is now recognized for its ability to add a new dimension to interventional studies that cannot be obtained through measurement of variables alone.[ 1] Qualitative research was initially used in psychological studies when researchers found it tedious to evaluate human behavior in numeric. Since then, qualitative research is used in other research fields as well. In clinical research, qualitative approach can help view the data more extensively. It strengthens clinical trials by enhancing user involvement in it.

Three broad categories of qualitative research of interest exists in clinical research: Observational studies, interview studies and documentary/textual analysis of various written records.[ 2] Qualitative research gives voice to the participants in the study.[ 1] It permits the participants to share their experiences of the effects of the drug of interest. This can open our eyes to new aspects of the study and help modify the design of the clinical trial. Qualitative study enhances the involvement of everyone related to the study. The researcher works on the social parameters in addition to the quantitative measures in the study. The subjects also have an empowering experience in the study. They have an active role in the study and can voice their individual benefits and harms of the study. In addition, with qualitative methods, the relationship between the researcher and the participant is often less formal than in quantitative research.

Qualitative research can have a major contribution in health research. In clinical trials, qualitative research can have a great impact on data collection, its analysis and the interpretation of results. Qualitative studies should be well-designed and the aims, procedures of the study should be meticulously adjudicated. Study should have pre-determined methods to nullify research bias. When combined with quantitative measures, qualitative study can give a better understanding of health related issues. The perspectives in clinical research should highlight advances in qualitative research as well, to optimize quality and utility of this method of research.

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Sir, Scientific research is based upon finding a solution to a particular problem one can identify. There are various methods of formulating a research design for the study. Two broad approaches of data collection and interpretation in research are qualitative and quantitative research.