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Find out what you can do to look after your physical and mental health.

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https://schools.au.reachout.com/articles/wellbeing-and-resilience

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Wellbeing and resilience are important in preventing the onset of mental health problems as well as potentially lessening the severity of existing mental health problems. Wellbeing and resilience are vital to developing efficient problem-solving skills, building and maintaining interpersonal relationships and realistic goal setting, all of which greatly enhance an individual's ability to perform and contribute meaningfully in daily life.

What is wellbeing and resilience?

Good health is about more than just the absence of sickness. Focusing on wellbeing and building resiliency is important in establishing a holistic approach to health, addressing both physical and psychological states.

The World Health Organisation defines wellbeing as "the state in which an individual realises his or her own abilities, can cope with normal stresses of life, can work productively, and is able to make a contribution to his or her own community". Wellbeing involves having positive self image and esteem.

Resilience, which is directly related to wellbeing, is about having the ability to cope with and adapt to new situations. Having a sense of resilience and positive wellbeing enables a person to approach other people and situations with confidence and optimism, which is especially important for young people given the enormous changes that occur with the transition into adolescence and adulthood.
Signs of resilience and wellbeing:

confidence to approach new situations and approach new people

realistic optimism

avoiding constant self-blame

ability to set goals

positive self image and self esteem.

How can students focus on wellbeing and resilience?

In a practical sense developing and improving on setting realistic and achievable goals, problem solving and social skills all contribute to wellbeing and resiliency. Other important skills include identifying and becoming aware of one's own strengths and weaknesses.

1. Setting realistic, achievable and measurable goals is a great way to promote self-efficacy. Setting goals should be motivating. One way to increase motivation (and minimise feeling pressure or 'failure') is to set sub-goals, smaller and achievable related targets.

2. Understanding and avoiding negative self-talk as well as actively practicing positive self-talk, is an important tool for building self-esteem. Self-talk, which is essentially internal reflections on personal ability and/or image, can greatly influence self esteem and perceptions about personal ability.

3. Developing and focusing on interpersonal skills, especially learning how to engage with people from different backgrounds, is a valuable tool for young people that will enhance self esteem and ability to maintain personal and fulfilling relationships.

What can teachers do?

create safe and supportive learning environments

provide consistent guidelines for behaviour

model appropriate responses and behaviours

provide opportunities for goal setting, success, failure and reflection.

ReachOut.com resources on wellbeing and resilience

How to help

Talk to young people about their mental health in terms of building strength and resilience.

Suggest self-awareness and resilience strategies, such as mindfulness meditation. Try the Smiling Mind app.

Recommend ReachOut.com content on resilience and wellbeing.

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Wellbeing and resilience are important in potentially preventing the onset of and lessening the severity of existing mental health problems.

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https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01226/full

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Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan

Nazarbayev University School of Medicine, Nur-Sultan, Kazakhstan

Psychological Counseling Center, Nazarbayev University, Nur-Sultan, Kazakhstan

The purpose of this study is to map the literature on mental health and well-being of university students using metadata extracted from 5,561 journal articles indexed in the Web of Science database for the period 1975-2020. More specifically, this study uses bibliometric procedures to describe and visually represent the available literature on mental health and well-being in university students in terms of the growth trajectory, productivity, social structure, intellectual structure, and conceptual structure of the field over 45 years. Key findings of the study are that research on mental health and well-being in university students: (a) has experienced a steady growth over the last decades, especially since 2010; (b) is disseminated in a wide range of journals, mainly in the fields of psychology, psychiatry, and education research; (c) is published by scholars with diverse geographical background, although more than half of the publications are produced in the United States; (d) lies on a fragmented research community composed by multiple research groups with little interactions between them; (e) is relatively interdisciplinary and emerges from the convergence of research conducted in the behavioral and biomedical sciences; (f) tends to emphasize pathogenic approaches to mental health (i.e., mental illness); and (g) has mainly addressed seven research topics over the last 45 years: positive mental health, mental disorders, substance abuse, counseling, stigma, stress, and mental health measurement. The findings are discussed, and the implications for the future development of the field are highlighted.

Introduction

The entrance to the university marks a period of transition for young people. Through this transition, students face new challenges, such as making independent decisions about their lives and studies, adjusting to the academic demands of an ill-structured learning environment, and interacting with a diverse range of new people. In addition, many students must, often for the first time, leave their homes and distance themselves from their support networks ( Cleary et al., 2011). These challenges can affect the mental health and well-being of higher education students. Indeed, there is evidence that a strain on mental health is placed on students once they start at the university, and although it decreases throughout their studies ( Macaskill, 2013; Mey and Yin, 2015), it does not return to pre-university levels ( Cooke et al., 2006; Bewick et al., 2010). Also, the probabilities of experiencing common psychological problems, such as depression, anxiety, and stress, increase throughout adolescence and reach a peak in early adulthood around age 25 ( Kessler et al., 2007) which makes university students a particularly vulnerable population.

The interest in mental health and well-being in university students has grown exponentially in the last decades. This is likely due to three interrelated challenges. First, although university students report levels of mental health similar to their non-university counterparts ( Blanco et al., 2008), recent studies suggest an increase and severity of mental problems and help-seeking behaviors in university students around the world in the last decade ( Wong et al., 2006; Hunt and Eisenberg, 2010; Verger et al., 2010; Auerbach et al., 2018; Lipson et al., 2019). Some researchers refer to these trends as an emerging "mental health crisis" in higher education ( Kadison and DiGeronimo, 2004; Evans et al., 2018). Second, psychological distress in early adulthood is associated with adverse short-term outcomes, such as poor college attendance, performance, engagement, and completion (e.g., King et al., 2006; Antaramian, 2015), and others in the long term, such as dysfunctional relationship ( Kerr and Capaldi, 2011), recurrent mental health problems, university dropout, lower rates of employment, and reduced personal income ( Fergusson et al., 2007). Third, there is a widespread agreement that higher education institutions offer unique opportunities to promote the mental health and well-being of young adults as they provide a single integrated setting that encompasses academic, professional, and social activities, along with health services and other support services ( Eisenberg et al., 2009; Hunt and Eisenberg, 2010). However, the majority of university students experiencing mental health problems and low levels of well-being are not receiving treatment ( Blanco et al., 2008; Eisenberg et al., 2011; Lipson et al., 2019) and, while universities continue to expand, there is a growing concern that the services available to provide support to students are not developing at an equivalent rate ( Davy et al., 2012).

In response to the increasing volume of research on the mental health and well-being of university students, there have been several attempts to synthesize the accumulating knowledge in the field and to provide an illustration of the theoretical core and structure of the field using traditional content analysis of the literature (e.g., Kessler et al., 2007; Gulliver et al., 2010; Hunt and Eisenberg, 2010; Sharp and Theiler, 2018). This study aims to extend the understanding of mental health in university students by providing a bird's eye view of the research conducted in this field in recent decades using a bibliometric approach. Bibliometric overviews provide an objective and systematic approach to discover knowledge flows and patterns in the structure of a field ( Van Raan, 2014) reveal its scientific roots, identify emerging thematic areas and gaps in the literature ( Skute et al., 2019) and, ultimately, contribute to moving the field forward. Accordingly, this study employs several bibliometric indicators to explore the evolution of the field based on publication and citation trends, key actors and venues contributing to the advancement of research on mental health and well-being of university students, and the structure of the field in terms of patterns of scientific collaborations, disciplines underlying the foundations of the field, and recurrent research themes explored in the literature. This is important because, despite significant advances in the field, research on mental health and well-being remains a diverse and fragmented body of knowledge ( Pellmar and Eisenberg, 2000; Bailey, 2012; Wittchen et al., 2014a). Indeed, mental health and well-being are nebulous concepts and their history and development are quite intricate, with a multitude of perspectives and contributions emerging from various disciplines and contexts (see section "Conceptualization of Mental Health, Mental Illness, and Well-Being: An Overview"). Therefore, mapping research on mental health and well-being in university students is essential to identify contributions and challenges to the development of the field, to help guide policy, research, and practice toward areas, domains, populations, and contexts that should be further explored, and to provide better care of students at higher education institutions ( Naveed et al., 2017).

Conceptualization of Mental Health, Mental Illness, and Well-Being: An Overview

This section provides an overview of the different perspectives adopted in the literature to conceptualize mental health, well-being, and other relevant constructs in order to identify the glossary of key terms that will be used in the search strategy to create a comprehensive corpus of documents on mental health and well-being in university students for this bibliometric review.

Perspectives on Mental Health and Mental Illness

There is no general agreement on the definition of mental health. For a long time, the term mental health has been used as a euphemism for mental illness ( Manwell et al., 2015). However, mental health and mental illness are regarded as distinct constructs nowadays and two main perspectives differentiating between mental health and illness are available in the literature. The continuum approach considers that mental health and mental illness are the two opposite poles of a continuum. Thus, there are various degrees of health and illness between these poles, with most of us falling somewhere in between. The categorical approach, on the other hand, represents mental health and illness as a dichotomy. People who manifest mental illness symptoms would belong to that category and labeled correspondingly, while those absent of these symptoms can be considered as mentally healthy ( Scheid and Brown, 2010).

Disciplinary Approaches to the Conceptualization of Mental Health/Illness

Conceptualizations of mental health/illness are largely dependent on the theoretical and paradigmatic foundations of the disciplines from which they emerge. In this context, the field has progressively evolved through the accumulation of knowledge generated in a diverse range of disciplines in the biomedical, behavioral, and social sciences. Biomedical disciplines are grounded in the medical paradigm focused on disease and (ab)normality and often emphasize dichotomous conceptions of mental health/illness ( Scheid and Brown, 2010). Research on mental health and well-being in this domain has been traditionally conducted from a psychiatric perspective, which aims to understand the dysfunctionality in the brain that leads to psychiatric symptoms and to also offer a pharmacological treatment to correct neuronal dysfunctions. Consequently, psychiatrists have historically considered mental health as a disease of the brain (e.g., depression), similar to any other physical disease, caused by genetic, biological, or neurological factors ( Schwartz and Corcoran, 2010). While the prevalence of psychiatric approaches to mental health is currently incontestable, the development of other biomedical disciplines has tremendously contributed to the progression of the field in recent decades. For example, Insel and Wang (2010) argue that insights gained from genetics and neuroscience contribute to the reconceptualization of "the disorders of the mind as disorders of the brain and thereby transform the practice of psychiatry." (1979). In addition to that, other disciplines such as behavioral medicine have made important contributions to the field, although it has recently argued that mental health and behavioral medicine should be as two separate fields ( Dekker et al., 2017).

Within the behavioral sciences, the study of mental health focuses on the distinct psychological processes and mechanisms that prompt thoughts, feelings, and behaviors ( Peterson, 2010). Clinical psychology has the longest tradition in the psychological study of mental health and tends to focus on the assessment and treatment of mental illness and disorders that can alleviate psychological distress or promote positive states of being ( Haslam and Lusher, 2011). However, significant contributions to the field have also emerged from other branches of psychology less focused on psychopathology, including personality and social psychology, psychoanalysis, humanistic psychology, and cognitive psychology ( Peterson, 2010). Despite the diversity of theories, principles, and methodological approaches to understanding mental health within the behavioral sciences, these disciplines acknowledge that mental health have a biological basis and reside in the social context, and tend to prioritize continuum approaches to mental health ( Scheid and Brown, 2010).

Perspectives from the social sciences complement the biomedical and behavioral approaches by considering the influence of social and cultural environments in mental health/illness ( Horwitz, 2010). For example, sociologists are interested in how social circumstances (e.g., level of support available) affect levels of mental health/illness and how social structures shape the understanding and response to mental health issues [see Compton and Shim (2015) for an overview of the social determinants of mental health]. Similarly, medical anthropologists attend to the mental health beliefs and practices that form the cultural repertory within and across populations ( Foster, 1975). Beyond sociology and anthropology, social researchers in the fields of business and economics, family and ethnic studies, and educational research have also played a key role in advancing research on mental health in different directions.

The Importance of the Context in Mental Health

Certainly, most notions of mental health/illness in the literature derive from prevailing psychiatric and psychological traditions developed in Western countries ( Gopalkrishnan, 2018). However, cultural values and traditions do shape how mental health and mental illness are conceptualized across contexts ( Vaillant, 2012). In this regard, Eshun and Gurung (2009) pointed out that "culture influences how individuals manifest symptoms, communicate their symptoms, cope with psychological challenges, and their willingness to seek treatment." (4). Fernando (2019) argued that issues related to the 'mind' developed and are often interpreted very differently in non-Western and Low- and Middle-Income Countries (LMICs). For example, cultures explain the manifestation of certain feelings and behaviors based on a range of motives including biological, psychological, social, religious, spiritual, supernatural, and cosmic. Failure to acknowledge alternative non-Western approaches to mental health and mental illness has resulted in imbalances of knowledge exchange and the permeation of dominating Western narratives into LMICs (i.e., so-called medical imperialism) ( Timimi, 2010; Summerfield, 2013). To address this issue, scholars have advocated for a greater willingness to embrace pluralism in the conceptualization of mental health and illness, which might help people to engage with particular forms of support that they deem to be appropriate for them, and to explore how knowledge and practices developed in LMICs can benefit those living in higher-income countries (i.e., knowledge "counterflow") (see White et al., 2014).

Prioritizing Positive Mental Health: The Science of Well-Being

Despite the diversity of disciplinary and contextual approaches to mental health, current definitions of mental health have two things in common. First, mental health is considered from a biopsychosocial point of view that incorporates biological, psychological, and social factors. Second, mental health implies something beyond the absence of mental illness (e.g., Bhugra et al., 2013; Galderisi et al., 2015). An example is the definition by the World Health Organization which refers to mental health as "a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community" ( World Health Organization, 2004). This definition contributed to substantial progress in research and practice in the field as it expanded the notion of mental health beyond the absence of mental illness and integrated the presence of positive features ( Galderisi et al., 2015).

Research on positive mental health is relatively new but has grown rapidly in the last decades fueled by advocates of positive medicine and psychology, who have argued for a change of paradigm from medical and psychopathological-oriented models of mental health that focus on disorders and illness toward more strength-based approaches, which pay more attention to what is right about people and positive attributes and assets ( Kobau et al., 2011). In this regard, the term mental well-being has been progressively incorporated into the study of mental health to account for the positive aspects of mental health beyond the absence of negative factors. While there is not a universally accepted definition of well-being, two perspectives have dominated the discourses on well-being in the literature: subjective well-being (SBW) and psychological well-being (PWB). SWB is based on hedonic perspectives of pleasure and represents "people's beliefs and feelings that they are living a desirable and rewarding life" ( Diener, 2012). SBW is strongly linked with the idea of happiness and is typically understood as the personal experience of high levels of positive affect, low levels of negative affect, and high satisfaction with one's life ( Deci and Ryan, 2008). PWB is grounded in Aristotelian ideas about eudaimonia, i.e., self-realization, with the ultimate aim in life being to strive to realize one's true potential ( Ryff and Singer, 2008). PWB has been broadly defined as a state of positive psychological functioning and encompasses six dimensions: purpose in life (i.e., the extent to which respondents felt their lives had meaning, purpose, and direction); autonomy (i.e., whether they viewed themselves as living in accord with their own convictions); personal growth (i.e., the extent to which they were making use of their personal talents and potential); environmental mastery (i.e., how well they were managing their life situations); positive relationships (i.e., the depth of connection they had in ties with significant others); and self-acceptance (i.e., the knowledge and acceptance they had of themselves, including awareness of personal limitations) ( Ryff, 1989).

Integrating Mental Health, Mental Illness, and Well-Being

The contribution of positive mental health frameworks to the advancement of the field has been undeniable. However, definitions that overemphasize positive emotions and productive functioning as key indicators of mental health have been recently challenged because of the potential they have to discriminate against individuals and groups that, for example, might not be able to work productively or function within the environment because of individual physical characteristics or contextual constraints ( Galderisi et al., 2015). To address these issues, Keyes has successfully integrated the notions of mental illness, mental health, well-being, and other related terms in the literature into a conceptual framework that allows for a more comprehensive understanding of mental health ( Keyes, 2005, 2007; Keyes and Michalec, 2010). The model argues that neither pathogenic approaches focusing on the negative (e.g., mental illness) nor salutogenic approaches focusing on the positive (e.g., well-being) can alone accurately describe the mental health of a person ( Keyes and Michalec, 2010). Instead, the model proposes that mental illness and well-being represent two correlated but differentiated latent continua in defining mental health. More specifically, mental illness and well-being lie on two separate spectra, the first going from absent to present mental illness and the second running from low to high well-being ( Slade, 2010). The absence of mental illness, therefore, does not necessarily imply high levels of well-being. Correspondingly, low levels of well-being do not always indicate the presence of mental illness. Further, in this model, mental health is defined as not only the absence of mental illness, not the mere presence of high well-being. Complete mental health (i.e., flourishing) is a result of experiencing low mental illness and high levels of well-being. Incomplete mental health (i.e., languishing), on the other hand, refers to the absence of mental illness symptoms and low reported levels of well-being. Two other conditions are possible within this framework. Incomplete mental illness (i.e., struggling) refers to high levels of well-being accompanied by high mental illness symptoms. Lastly, complete mental illness (i.e., floundering) accounts for low levels of well-being and high mental illness symptoms ( Keyes and Lopez, 2002).

The Present Study

In light of the complexity of the constructs of mental health and well-being and the multiple theoretical, disciplinary, and contextual approaches to their conceptualization, this study seeks to map out the terrain of international research and scholarship on mental health and university students for the period 1975-2020. More specifically, this study aims to provide new insights into the development and current state of mental health research in university students by mapping and visually representing the literature on mental health and well-being of university students over the last 45 years in terms of the growth trajectory, productivity, and social, intellectual, and conceptual structure of the field. First, the study describes the development of research mental health and well-being in university students examining the trends in publication and citation data between 1975 and 2020 (i.e., growth trajectory). Second, the study identifies the core journals and the research areas contributing most to the development of the field, as well as the key authors and countries leading the generation and dissemination of research on mental health and well-being in university populations (i.e., productivity). Third, the study outlines the networks of scientific collaboration between authors, and countries (i.e., social structure). Fourth, the scientific disciplines underlying the intellectual foundations of research on mental health and well-being in university settings (i.e., intellectual structure) are uncovered. Fifth, the study elucidates the topical foci (i.e., conceptual structure) of the research on the mental health and well-being of university students over the last 45 years.

Materials and Methods

A bibliometric approach was used in this study to map the literature on mental health and well-being in university students over the last 45 years using metadata extracted from four indexes of the Web of Science (WoS): The Science Citation Index-Expanded (SCI-Expanded); the Social Sciences Citation Index (SSCI); the Arts & Humanities Citation Index (A&HCI); and the Emerging Sources Citation Index (ESCI). Several reasons justified the selection of the WoS database in this study. First, the WoS remains as the standard and most widely used for bibliometric analysis ( Meho and Yang, 2007). Second, the WoS is a multidisciplinary database and includes publications on mental health and well-being emerging from distinctive research areas and disciplines published in more than 20,000 journals ( McVeigh, 2009). Using specialized databases such as PubMed would introduce biases into the search strategy favoring biomedical research disciplines. Still, it is important to note that interdisciplinary databases such as WoS and Scopus discriminate against publications in the Social Sciences and Humanities and publications in languages other than the English language ( Mongeon and Paul-Hus, 2016), so the picture provided by WoS is still imperfect. Third, while other databases might provide wider coverage, WoS includes publication and citation information from 1900. For example, Scopus has complete citation information only from 1996 ( Li et al., 2010). Moreover, Google Scholar provides results of inconsistent accuracy in terms of citations, and citation analyses in PubMed are not available ( Falagas et al., 2008). Fourth, WoS has demonstrated better accuracy in its journal classification system compared to Scopus database ( Wang and Waltman, 2016).

The methodological approach used in this study is presented in Figure 1 and further elaborated in the following paragraphs.

Search Strategy

To create a comprehensive corpus of documents on the mental health and well-being of university students, three parallel searches were performed, which accounted for the multiple approaches and perspectives that have been used in the field, as identified in the Section "Conceptualization of Mental Health, Mental Illness, and Well-Being: An Overview." All the searches were conducted in the last week of January 2020. The first search aimed at capturing research on mental health broadly and included one single keyword in the topic field: ["mental health"]. The second search was implemented to capture research focusing on pathogenic approaches to mental health. Key terms used in the literature to refer to the negative side of mental health, as well as the most frequent mental health problems experienced by university students, were introduced in this search in the title field: ["mental illness," "mental disorder ∗," "mental distress," "psychological distress," "psychopathology," "depression," "anxiety," "stress," "suicide," "eating disorder ∗," "substance use"]. In the third search, keywords reflecting salutogenic approaches to mental health were input. These included terms related to mental health from a positive mental health perspective (i.e., well-being). These key terms were added in the title field and included the following: ["well-being," "wellbeing," "wellness," "life satisfaction," "happiness," "positive affect," "purpose in life," "personal growth," "self-determination"].

To retrieve research relevant only to higher education students, another set of keywords was imputed in all three searches in the title field. These included: ["university," "college," "higher education," "tertiary education," "post-secondary education," "postsecondary education," "undergrad ∗ student," "grad ∗ student," "master's student," "doctoral student," "Ph.D. student"]. The Boolean operator OR was used between keywords in all the three searches to secure a higher number of relevant hits. Also, asterisks were used as wildcards to account for multiple variations in several keywords (e.g., disorder and disorder-s). All searches were limited to journal articles published between 1975 and 2020 (both inclusive). No restrictions on language were implemented in the search.

The search strategy retrieved a total of 6,356 hits ( n search 1 = 2782; n search 2 = 2814, n search 3 = 760). After the removal of duplicates, 5,561 research articles were finally selected and retained for the study. For each of the documents obtained in the search, the authors extracted metadata about the title of the paper, the year of publication, the journal, the number of citations, and the authors' name, organization, and country. Also, the title, the abstract, the author's keywords, and cited references were retrieved.

Data Analysis Procedures

The corpus of the literature was then analyzed using descriptive and bibliometric approaches to provide an overall picture of the evolution and current state of the research on mental health and wellbeing in university settings. Frequency counts of the number of publications and citations per year were obtained to describe the growth trajectory of research on the mental health and well-being of university students. Rank ordered tables were produced to describe the productivity of the field in terms of core journals and research areas, as well as leading scholars and countries contributing to the development of the field.

Bibliometric analyses in VOSViewer software were implemented to examine and visually represent the social, intellectual, and conceptual structure of the field. VOSViewer is a freely available computer software for viewing and constructing bibliometric maps. In VOSViewer, the units of analysis are journals, publications, citations, authors, or countries, depending on the focus of the analysis. The units of analysis are represented in the maps as circular nodes. The size of the node accounts for volume (e.g., number of publications in the dataset by an author) and the position represents the similarity with other nodes in the map. Closer nodes are more alike than nodes far apart from each other. The lines connecting nodes represent the relationship between nodes and their thickness indicates the strength of that relationship. Finally, the color of the node denotes the cluster to which each node has been allocated. Nodes are clustered together based on relatedness ( Van Eck et al., 2010). The software uses a distance-based approach to constructing the bibliometric maps in three steps ( Van Eck and Waltman, 2014). In the first step, the software normalizes the differences between nodes. In the second step, the software builds a two-dimensional map where the distance between the nodes reflects the similarity between these nodes. In the third step, VOSViewer groups closely related nodes into clusters ( Van Eck and Waltman, 2014).

A series of co-authorship analyses were performed to examine the social structure of research on mental health and well-being in university students. In these analyses, the units of analysis were authors and countries/territories. Each node in the map represents an author or a country/territory and the lines connecting them reflect the relationship between nodes. Clusters represent networks of scientific collaboration, which might be interpreted as groups of authors or countries frequently publishing together (e.g., research groups in the case of authors).

Co-citation analysis of journals was implemented to explore the intellectual structure of the field. Here, the units of analysis were journals in the dataset and the map reflects co-citation relationships between journals. Two journals are co-cited if there is a third journal citing these two. The more times a pair of journals are cited by other journals, the stronger their co-citation relationship will be. Frequently co-cited journals are assumed to share theoretical and semantical grounds. Therefore, in our study, clusters of frequently co-cited journals can be interpreted as disciplines underlying the foundations of research on mental health and well-being in university students.

Finally, a co-occurrence analysis of keywords was used to uncover the conceptual structure of the field. The units of analysis, in this case, were the authors' keywords. The more often two keywords appear in the same record, the stronger their co-occurrence relationship. Clusters of co-occurring keywords represent in this study the topical foci (i.e., knowledge base) that have been addressed in the literature in mental health and well-being in university students in the last 45 years.

Findings and Discussion

Growth Trajectory: Evolution of Publications and Citations in the Field

The developmental patterns of a particular field can be well demonstrated by trends in publications and citations. The 5,561 publications in the dataset have been cited 87,096 times, with an average of 15.6 citations per item. Figure 2 shows the growth trajectory of publication data of research on mental health and well-being in university students from 1975 to January 2020. Overall, the trends demonstrate a gradual increase in the scholarly interest in the mental health of university students over the last 45 years that can be organized in three stages: an emergence stage, in which publications rose slowly (1975-2000); a fermentation stage, with a notable increase in publications in the field (2000-2010); and a take-off stage, during which the number of records published per year in the field has almost risen 10 times (2010-2020). The steady increase of publications in the last 15 years coincides with the first calls for attention on the increase and severity of mental problems and help-seeking behaviors of college students ( Kadison and DiGeronimo, 2004; Evans et al., 2018), potentially indicating a growing interest in exploring the epidemiology of mental disorders and the role of universities in promoting the mental health and well-being of students. A similar pattern has also been observed in a recent bibliometric study examining global research on mental health both in absolute terms and as a proportion of all papers published in medicine and across disciplines, which certainly reflects an increase in the general interest in the field ( Larivière et al., 2013).

Productivity I: Core Journals and Research Areas

In total, 1,560 journals published the 5,561 records included in the dataset. Table 1 presents the ten core journals in the field. The Journal of American College stands out as the main publication venue in the field, accumulating around 5% of the publications in the dataset ( n = 270). Psychological Reports and Journal of College Student Development also stand out, publishing 119 and 102 studies, respectively. The Journal of Counseling Psychology ranks fourth in the list with 83 records. Despite being an interdisciplinary and relatively young journal, Plos One appears in the top five journal publishing research on mental health and well-being in university students.

The top research areas contributing to the publication of research on the mental health and well-being of university students are presented in Table 2. Nearly half of the records in the dataset are published in psychology journals. Another influential research area in the field is psychiatry, which captures almost 20% of the publications. Journals on education and educational research also accumulate a considerable number of publications in the field (15%). Other relevant research areas in the field are connected with health and medicine, including public environmental occupational health, substance abuse, general internal medicine, neurosciences neurology, health care sciences services, and nursing. Finally, the field is also grounded, although to a lower extent, in the publications emerging from journals in the social sciences, family studies, and social work research.

All in all, the productivity analysis for journals and research areas showed that most research on mental health and well-being in university students is disseminated in journals in the "psy disciplines"' (i.e., psychology and psychiatry) ( McAvoy, 2014), which is consistent with previous research on mental health in general populations (e.g., Haslam and Lusher, 2011). However, our findings demonstrated that the volume of research in psychology doubles that of research emerging from psychiatric journals. This contrasts with the findings by Haslam and Lusher (2011), who demonstrated that psychiatry journals had a greater influence on mental health research compared to clinical psychology journals and that psychiatry journals accumulate a higher volume of research and citations on mental health research. This is probably because our study includes publications emerging from all branches of psychology, unlike the study by Haslam and Lusher, which included only journals in the field of clinical psychology. Additionally, mental health services in higher education are typically provided by counseling centers led and staffed by non-medical professionals (e.g., psychologists, social workers, counselors, and family therapists) who tend to adopt developmental models of practice grounded in the behavioral sciences and focused on adjustment issues, vocational training, employment, and other personal needs rather than diagnosis and symptom reduction, more common in the biomedical sciences (i.e., psychiatry) ( LeViness et al., 2018; Mitchell et al., 2019).

Productivity II: Leading Authors and Countries/Territories

The 5,561 publications in the dataset were published by a total of 16,161 authors from 119 countries worldwide. Table 3 shows the researchers with the highest number of publications in the field. D. Eisenberg appears as the most productive researcher, followed by K. Peltzer and S. Pengpid. Authors on the list come from diverse geographical backgrounds. Five of the authors work at three different American universities (University of Michigan, Harvard Medical School, and Boston University), two researchers work at KU Leuven University (Belgium), and two other authors are affiliated to the same two universities in Thailand and South Africa. Other prolific researchers are affiliated with higher education institutions in the Netherlands, Egypt, and Germany.

Countries and territories leading research on mental health and well-being of university students are presented in Table 4. The United States is the indisputable leader in this field, publishing more than half of the records in the dataset. This is nearly 10 times the number of publications produced in China, which occupies the second position in the ranking and accounts for nearly 6% of the volume of research in the dataset. Three predominantly English speaking countries/territories complete the top five of the ranking: Canada (265 records), Australia (254), and England (243). The rest of the countries in the list are situated in Europe (Spain, Germany, Turkey), Western Asia (Iran), Africa (South Africa), and East Asia (Japan), which demonstrates that research on college students' mental health and well-being is a matter of concern in different regions of the world, at least to some extent.

Overall, the productivity analysis for authors and countries indicated that the research of mental health and well-being of university students occurs in a variety of locations around the world, especially in developed countries, and in a very prominent way, in the United States. This is not surprising since it is in those countries where better infrastructures and more abundant resources for research are available ( Wong et al., 2006), and a more lasting tradition in the study of mental health, in general, exists ( Gopalkrishnan, 2018). However, Larivière et al. (2013) found that the productivity of the United States on mental health research has dropped significantly and remained stable in other two English speaking countries (the United Kingdom and Canada) since 1980. On the contrary, the number of publications from European countries and the five major emerging national economies (Brazil, Russia, India, China, and South Africa), has experienced remarkable growth, and collectively account nearly for half of the publications in the field. Still, the predominance of knowledge generated in the developed world today, which tends to be grounded on psychiatric and psychological perspectives, might be eclipsing non-traditional views on mental health and well-being that are popular in other regions of the world and, therefore, limiting the development of effective initiatives that align better with local norms, values, and needs in LMICs ( Timimi, 2010; Summerfield, 2013).

Social Structure: Networks of Scientific Collaboration

Research collaboration is regarded as an indicator of quality research and a means to improve research productivity and academic impact (i.e., citations) ( Kim, 2006; Abramo et al., 2009). In particular, international research collaboration is considered a key contributor to the social construction of science and the evolution of scientific disciplines ( Coccia and Wang, 2016). There is recent evidence that national and international research collaborations have been accelerating in recent years ( Gazni et al., 2012; Wagner et al., 2015), especially in applied fields such as medical and psychological disciplines ( Coccia and Bozeman, 2016). In this study, co-authorship analyses were performed to find out patterns in the scientific collaboration between researchers and countries/territories on the mental health and well-being of university students.

Figure 3 demonstrates collaborative ties among authors who published at least 5 articles in the dataset ( n = 179). The map shows the existence of multiple productive collaborative networks of five or more researchers contributing to the development of the field. The largest collaboration network (red cluster) represents an international research group composed of 15 scholars affiliated to universities in the United States, Belgium, and Netherlands. This cluster groups some of the leading scholars in the field, including R. P. Auerbach, R. Brauffaerts, R. C. Kressler, and P. Cuijpers. Moreover, researchers in this cluster lead The WHO World Mental Health International College Student (WMH-ICS) Initiative, a large scale international project aimed at promoting the mental health and well-being of college students around the world through generating epidemiological data of mental health issues in university students worldwide, designing web-based interventions for the prevention and promotion of mental health, and disseminating evidence-based interventions ( Cuijpers et al., 2019). The second biggest cluster (green) represents an intra-national research network that includes 10 researchers from eight different higher education institutions in the United States. The dark blue cluster represents an institutional collaborative network, including nine researchers from the School of Public Health, Puerto Rico. Other prominent clusters in the map represent collaborative research networks between eight (olive color) and seven researchers (turquoise, violet, orange, and mellow mauve). This contrasts, however, with the limited collaboration that exists between clusters. Only four of the clusters on the map demonstrate some kind of scientific collaboration in the field (light blue, pink, brown, and yellow).

Cross-country collaboration networks in mental health and well-being of university students study are presented in Figure 4. Research collaborations between countries with 20 or more publications were considered in this analysis ( n = 45). The United States occupies the central position of the map and shares collaborative ties with all other countries/territories, forming a cluster together with China, South Korea, and Taiwan. Overall, the results suggest that international collaborations in the field are framed to a large extent by cultural, linguistic, and geographical proximity. For instance, the largest cluster (red) is formed by two European countries (Spain and Portugal) and other South American countries with whom they share historical and cultural backgrounds. Other European countries form the purple cluster. Similarly, the blue cluster clearly brings together predominantly English-speaking countries and territories, while the green cluster agglomerates a range of Asian countries.

Collectively, the results of our study suggest that research collaboration in the field of mental health and well-being in university students remains relatively scarce and localized to date. The social structure of the field at the author level could be described as an archipelago formed by a large number of islands (research groups) of different composition and size but with few bridges connecting them, which suggests a relatively fragmented research community. Moreover, while the existence of international collaborative networks was evident in the analysis, they seem to be formed within national borders, between researchers in neighboring countries/territories, or between countries that share cultural, linguistic, and historical heritages. This may be due to the important role that cultural and traditional values play in the conceptualization of mental health and well-being across contexts ( Eshun and Gurung, 2009; Vaillant, 2012; Fernando, 2019). Also, language differences, divergent cross-national institutional and organizational traditions, and increased costs of extramural collaboration, have been found to complicate the formation and continuity of research partnerships in health research ( Hooper et al., 2005; Freshwater et al., 2006). Nevertheless, limited within- and between-country research collaboration arguably poses challenges to the development of a field in terms of lost opportunities to challenge assumptions taken for granted and move toward fresh perspectives, push boundaries in methods and techniques, meet diverse groups of people from differing cultures and get immersed in those cultures, share information, resources, and skills, and address common mental health problems through the pooling of resources ( Rolfe et al., 2004; Freshwater et al., 2006).

Intellectual Structure: Disciplines Underlying the Foundations of the Field

Interdisciplinarity is considered as a valuable approach to address the complex and multidimensional nature of health and well-being ( Mabry et al., 2008). Buckton (2015) argues that the integration of medical, psychological, and social sciences have contributed to generate "new insights into theory, practice, and research in mental health and development." (3). To examine the disciplines underlying research on the mental health and well-being of university students, a journal co-citation analysis was performed. In this analysis, only journals with at least 50 citations were considered ( n = 593). The nodes on the map represent journals and their size reflects the number of co-citation relationships with other journals. Colors account for journal clusters, which agglutinate journals with higher co-citation relationships and stronger semantic connectedness. Clusters were interpreted and labeled accounting for the WoS categorization of the journals with the highest co-citation links within each cluster. For example, if the Journal of Personality and Social Psychology, the Journal of Counseling Psychology, and Personality and Individual Differences clustered together, this group was interpreted as the personality, social, and counseling psychology cluster.

In general, the findings of this study suggest that research on mental health and well-being in university students is interdisciplinary, to a certain extent, and mainly emerges from the convergence of research conducted in the behavioral and biomedical sciences, as it has been suggested elsewhere ( Schumann et al., 2014; Wittchen et al., 2014b). More specifically, the map shows that the research in the mental health and well-being of university students is constructed through the integration of knowledge generated in five interconnected disciplines (see Figure 5). To the left of the map, the red cluster integrates journals on personal, social, and counseling psychology. To the right, the blue cluster represents the contribution of psychiatric journals to research to the formation and development of the field. At the top, the yellow cluster groups journals on substance abuse and issues related to alcohol consumption, addiction, and interpersonal violence. At the bottom of the map, journals covering topics on eating behaviors, sleep, and other issues related to physical health converge on the green cluster. At the center of the map is the purple cluster, which includes journals in the area of clinical psychology and behavioral therapy.

More broadly, the findings suggest that biomedical sciences contribute to a large extent to the composition of the field. Psychiatric research emerged in our study as an obvious building block in the study of university students' mental health and well-being, which is not surprising considering the historical contributions of biomedical disciplines to mental health research ( Schwartz and Corcoran, 2010). Within the behavioral sciences, personality and social psychology, which explores processes and mechanisms through which social phenomena influence mental health and well-being ( Sánchez Moreno and Barrón López de Roda, 2003), appears as a key discipline underlying the foundations of the field. Surprisingly, clinical psychology journals occupy a central position in the map and demonstrate co-citation relationships with journals from all other clusters but make up the most dispersed network and account for a considerably lower volume of co-citation relationships in the field. This suggests that clinical psychology journals are more subordinate to journals in other disciplines in terms of citations flows, and ultimately, play a less unique role in research on the mental health and well-being of university students, as suggested by Haslam and Lusher (2011). Interestingly, research arising from the social sciences (e.g., sociology and anthropology) does not seem to make a distinctive contribution to the intellectual structure of the field, which suggests that the influence of social contexts and cultures on university students' mental health and well-being (e.g., inequality, social norms, public policies, cultural beliefs, and values) is an underexplored research area. Still, the density of co-citation network relationships within and between clusters is particularly noteworthy, considering the lack of common language between disciplines, the absence of a shared philosophy of practice on mental health, and the tensions between medical, psychological, and social explanations of mental distress ( Bailey, 2012).

Conceptual Structure: Topical Foci Addressed in the Literature Over the Last 45 Years

The topical foci of research on the mental health and well-being of university students during the 1975-January 2020 period are presented in Figure 6. The map offers a visual representation of the co-occurrence analysis of author keywords of all the publications included in the dataset. Only the most frequently occurring keywords (25+ occurrences) were considered in the analysis ( n = 84). Items that were not related to others and do not belong to the existing clusters were excluded. The size of the nodes indicates the occurrence of author keywords in the dataset and the thickness of edges represents the co-occurrence strength between pairs of keywords.

The most frequent keywords in the dataset, excluding students' descriptors (e.g., college students and university students), refer to common mental health challenges experienced by university students such as depression ( n = 612), anxiety ( n = 353), and stress ( n = 341). Salutogenic-related keywords such as well-being and life satisfaction occurred less often ( n = 138, n = 113, respectively), suggesting that pathogenic approaches to the exploration of mental health issues in higher education are more widespread. More broadly, seven general themes seem to summarize the topical foci of interest in the field of mental health and well-being of university students over the last 45 years. First, there has been a general interest in positive mental health, as denoted by frequently co-occurring key terms such as well-being, self-esteem, life satisfaction, social support, emotional intelligence, and happiness (red cluster). Second, mental disorders stand as another theme widely addressed in the literature, with a special emphasis on depression, anxiety, and to a lesser extent, suicide and suicidal ideation (green cluster). A third topical area in this field has been substance abuse, most predominantly alcohol consumption (blue cluster). The fourth theme reflects college counseling for mental health, including interventions and protective factors such as mindfulness, stress management, spirituality, and help-seeking (yellow cluster). Other topics reflected in the map are mental illness stigma (purple), stress (e.g., psychological distress and coping) (light blue), and mental health measurement (orange).

Conclusion

This study provides a comprehensive overview of the research on university students' mental health and well-being in the last 45 years using bibliometric indicators. In general, the results reveal interesting trends in the evolution of the field over the last four decades and promising scientific patterns toward a better understanding of the mental health and well-being of university students internationally. First, the interest in the mental health and well-being of university students has grown in the last decades and in a very significant way during the last 10 years, indicating that this area has not still reached its maturity period and will continue developing in the future. Second, research in the field is relatively interdisciplinary and emerges from the convergence of research conducted in several disciplines within the behavioral and biomedical sciences. Third, research in this field is produced by a community of productive researchers coming from several regions around the world, most notably in the United States, which secures a generation of scholars that will continue shaping the field in the years to come. Fourth, over the last 45 years, researchers have been able to address a multitude of research topics in the field, including positive mental health, mental disorders, substance abuse, counseling, stigma, stress, and mental health measurement.

However, this study also identified some issues that could be hindering the development of the study of the mental health and well-being of university students. For example, the research available overrepresents theoretical and disciplinary approaches from the developed world. Additional studies on the field from developing economies and LMICs are needed to provide a more comprehensive picture and ensure a fair representation of the multiple perspectives available in the field. Such studies would inform administrators and practitioners on how to broaden and enrich available programs and initiatives to promote mental health and well-being in higher education contexts in order to offer alternative forms of support that university students find appropriate for their social and cultural values. Moreover, the research community contributing to the development of the field is relatively fragmented. There are multiple research groups but little research collaborations between them and, at the international level, these connections tend to be limited by geographic, cultural, and language proximity. In this context, more actions like the WMH-ICS Initiative could provide a partial solution to this problem by strengthening national and international research partnerships and facilitating knowledge exchange across regions. Also, special issues in the core journals in the field inviting cross-cultural studies on the topic could contribute to promoting research collaboration across regions and research in less represented countries. The field would also benefit from a greater volume of research from the social sciences and humanities exploring the influence of social, cultural, economic, and educational factors on the conceptualization, manifestation, and experience of mental health and well-being. Moreover, more studies emerging from disciplines such as sociology, anthropology, business, and education, would likely increase the permeability of positive mental health concepts into the field and contribute to the promotion of salutogenic approaches to the study of mental health and well-being of university students.

This study has several limitations. First, publications were retrieved only from the WoS database, which limits the generalizability of the findings. Second, WoS provides stronger coverage of Life Sciences, Biomedical Sciences, and Engineering, and includes a disproportionate number of publications in the English language ( Mongeon and Paul-Hus, 2016). This could partially explain the low number of publications emerging from the Social Sciences, the Arts, and the Humanities, and research conducted in non-English speaking countries in the present study. Third, only journal articles were retrieved for analysis, excluding other relevant publications in the field such as reviews, book chapters, and conference proceedings. Future studies could replicate the findings of this study using alternative databases (e.g., Scopus and PubMed) or a combination of them, as well as different filters in the search strategy, to provide an alternative coverage of research conducted in the field. Nevertheless, we believe that the bibliometric approach used in this study offers novel insights about the development and current status of the field and some of the challenges that undermine its progression.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Author Contributions

DH-T and LI contributed to conception and design of the study, organized the database, and performed the statistical analysis. DH-T, LI, and JS wrote the first draft of the manuscript. NL, AC, AA, YN, and AM wrote the sections of the manuscript.

Funding

This research was funded by the Nazarbayev University Faculty-Development Competitive Research Grants Program (Reference Number 240919FD3902).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

References

LeViness, P., Bershad, C., Gorman, K., Braun, L., and Murray, T. (2018). The Association for University and College Counseling Center Directors Annual Survey. Available online at: https://www.aucccd.org/assets/documents/Survey/2018%20AUCCCD%20Survey-Public-June%2012-FINAL.pdf (accessed February 24, 2020).
Google Scholar

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Mental Health and Well-Being of University Students: A Bibliometric Mapping of the Literature

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The purpose of this study is to map the literature on mental health and well-being of university students using metadata extracted from 5,561 journal articles indexed in the Web of Science database for the period 1975-2020.

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https://www.frontiersin.org/articles/10.3389/feduc.2021.719539/full#:~:text=(2021)%20showed%20an%2015.3%25,just%20one%20month%20of%20lockdowns.

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William Mason High School, Mason, OH, United States

CSE Department, Miami University, Oxford, OH, United States

The American Psychological Association (APA) reports 81% of Gen Z teens (ages 13-17) have experienced more intense stress during the COVID-19 pandemic. This study uses a survey-based approach along with robust statistical analyses to identify key stressors from a set of students in a high school in Midwest United States. Our survey includes a broad range of stressors (15 explanatory variables) specific to high schoolers, controls (4 factors for pre-existing conditions), and mental health estimators (7 dependent variables) to identify changes in mental wellbeing during the pandemic. The results ( n = 107) show good consistency in our estimators (Cronbach's α = 0.78) and statistically significant (t = 0.636, p ≪ 0.001) degradation in the mental health. Correlation (r = 0.2, p = 0.034) and regression analysis showed that online learning (β1 = -0.96, p = 0.004) has the most influence on degradation in mental health, with some race-based differences. Exercise time helps reduce mental health degradation (β3 = -0.153, p = 0.037). Many other factors such as gender, homework time, school time, pre-existing mental health issues, and therapy did not have a significant influence on mental health degradation. Analysis of freeform feedback identified the following three recurring themes: increased stress due homework (13.2%), social isolation or lack of social interactions (8.5%), and lack of support for mental wellbeing (12.3%).

Introduction and Literature Review

The COVID-19 pandemic has had a significant worldwide impact on the lives of more than 1.6 billion students at its peak ( UNESCO 2021) and their schooling. Similar to many other countries, the United States also pursued a policy to lockdown schools, starting in mid-March 2020, to mitigate the pandemic. The lockdown caused the schools to rapidly adopt and pursue remote learning using video conferencing methods. The teachers, students, and their families have experienced several different challenges, including increased stress and anxiety that has led to degradation of mental health. The issues have been further compounded by stringent social distancing measures necessitated due to the lack of vaccines and high mortality rates (over two million deaths worldwide).

The issue of degrading mental health due to pandemics has been reported in the past ( Hawryluck et al., 2004; McAlonan et al., 2007; Lau et al., 2010; Xiang et al., 2014). The circumstances surrounding the COVID-19 pandemic have increased the stress and anxiety in people ( APA 2020; Tandon 2020; Wang et al., 2020), including high school students. Liang et al. (2020) report a much higher fraction of youth with psychological issues during the COVID-19 pandemic when compared to prior pandemics. The American Psychological Association (APA) reports [ American Psychological Association (APA), 2020] that nearly 81% of Gen Z teens Gen Z teens (ages 13-17) experience more intense stress due to COVID-19, associated with schooling. However, APA's report does not delve into the specific details of the stressors arising from the pandemic-related changes in schooling. The issues related to schooling and their impacts have been reported in many countries ( Dolean and Lervag, 2021).

China was the first country to be impacted by the COVID-19 pandemic. Hence, several studies analyzing the impact of the pandemic on the mental health of students have been reported. Hou et al. (2020) discuss the issue of mental health problems and suicidality among senior high school students in China during the COVID-19 pandemic. Their study involved 859 high school students (61.4% male and 79.4% below 16 years of age). Their results showed that depression, anxiety, PTSD, and suicidal ideation, and suicidal attempts were 71, 54.5, 85.5, 31.3, and 7.5% respectively. However, their study did not specifically focus on specific stressors, though they surmise that educational pressure, pronged school closure, and social stigma of COVID infection might be stressors. Nevertheless, they report that higher exercise frequency, a control in our study, correlated with fewer symptoms of depression and anxiety. Another cross-sectional study of 532 Chinese high school students by Zhang et al. (2020) used three types of questionnaires from prior studies and showed that nearly 20% of the students' mental health was impacted. Their analysis showed that resilience and positive coping were protective factors against depression, anxiety and stress symptoms. Another cross-sectional study ( Liang et al., 2020) of 584 youth (age 14-35) in China showed that 40.4% were prone to psychological problems and 14.4% had PTSD symptoms. In the context of COVID-19, their study suggested that mental health was related to educational level, employment, and using negative coping styles.

Asanov et al. (2021) discuss results from a cross-sectional survey of over 1,500 high school students (age 14-18) in Ecuador. Their survey has been conducted via phone interviews with the participants, focusing on topics related to access to remote learning technologies and their impacts. They used the MHI-5 index proposed by Veit and Ware (1983) that uses a five-point Likert-like scale to assess their mental health along with a cutoff score of 17 (max score of 25), with higher scores indicating better mental health. Their survey found 16% of participants had scores that indicate major depression. Similarly, a longitudinal study involving 442 last year high school in Greece by Giannopoulou et al. (2021) showed an 15.3% increase in depression, 17% increase in severe depression, 25.7% increase in anxiety, and 16.7% increase in severe anxiety after just one month of lockdowns. They use Generalized Anxiety Disorder Scale (GAD-7), which is a 7-item questionnaire with each item using a 4-point Likert-type scale to assess anxiety. They used a modified version of the Patient Health Questionnaire-9, that also uses a 4-point Likert-like scale, to assess depression.

Similar to the works of Asanov et al. (2021) and Giannopoulou et al. (2021), our study also used a 5-point Likert-like scale to assess the mental health of high school students. Although our survey is not longitudinal, it includes self-reported mental health scores before and during the pandemic, giving it some longitudinal characteristics. Since the survey was conducted during the pandemic, the self-reported mental health scores prior to the pandemic are subject to recall bias, similar to the analysis reported by Giannopoulou et al. (2021). Moreover, similar to Giannopoulou et al. (2021) we have also used Cronbach's alpha to establish good internal consistency of the survey results, which mitigates some of recall bias and increases confidence in our results and analysis.

Motivation for This Study

The aforementioned investigations on COVID-19 pandemic and its impact on mental health have focused on more general aspects of mental health. They identify some of the socioeconomic stressors and their impact on anxiety and depression. They don't delve into the specific stressors arising from the pandemic-related changes in schooling. In contrast, this study emphasizes several key stressors and controls along with their influence on mental health of high school students. Such a detailed analysis is exigent to identify and mitigate the impact of the stressors in order to improve the mental wellbeing of high school students. Our objective is to focus on the common stressors experienced by high school students. Hence, several stressors and controls were identified via a focus group of students and teachers. In addition, we include several controls to account for existing mental health issues. Our main research question is - has the mental health of high school students changed during the COVID-19 pandemic and what factors have contributed to the change in mental health? Moreover, we also aimed to elicit potential approaches to mitigate degradation in mental health from the participants.

Methods

This study uses a well-established scientific method for conducting a descriptive and exploratory research using a survey-based approach ( Bhattacherjee 2019). An overview of the method is summarized in Figure 1. The first phase of the investigation focused on eliciting the three main categories of variables, namely ① stressors (independent variables) that influence mental health, ② controls to account for any pre-existing mental health conditions, and ③ mental health estimators. This phase of the research first involved literature survey of peer reviewed articles to identify key variables to be surveyed. The APA surveys ( APA 2020) did not provide many parameters but suggested time invested on key activities such as school work, leisure, sports, and sleep. We also reviewed several instruments proposed in the literature for mental health assessments including MIH-5 ( Viet and Ware, 1983), GAD-7 ( Spitzer et al., 2006), PHQ-9 ( Johnson et al., 2002).

FIGURE 1. Overview of the methods used in this study.

At the time of this study, there was a paucity of technical articles pertaining to COVID-19 pandemic and its impact on mental health. Hence, we decided to pursue a focus group approach to obtain community input to elicit parameters and appropriately structure questions. The focus group consisted of a few high school students and two teachers. The focus group also motivated us to keep the questionnaire as short as possible and structure questions to be quickly understandable by students. Based on the focus group feedback, we have structured our mental health estimators along the lines of MIH-5, though our questions are different and included six questions, based on focus group feedback.

Overview of Our Instrument

The results from the literature survey and focus group analysis led to 29 questions pertaining to stressors, controls, and mental health estimators as summarized in Table 1. The complete survey administered to the participants is included in the supplements. Our survey started with a consent question (not shown in Table 1). The questions covered 15 stressors (i.e., independent variables) that influence mental health and were identified via literature survey ( APA, 2020; Zhang et al., 2020; Asanov et al., 2021). In addition, the questions included stressors related to online vs. in-person learning that were identified via our focus group discussions. Our instrument included four controls to account for any pre-existing mental health issues that could be unrelated to COVID-19, but could be exacerbated due to the pandemic. The survey then included questions on mental health estimators to assess the mental wellbeing before and during the pandemic ( Liang et al., 2020). The survey was administered during the pandemic and hence, the question on mental health prior to the pandemic is subject to recall bias, similar to other investigations ( Giannopoulou et al., 2021). Lastly, the survey included freeform questions for participants to share their opinions on how their mental health could be improved.

TABLE 1. Category and summary of parameters included in our instrument. Full questionnaire is included in the supplementary materials.

IRB and School Administration Approvals

The survey was designed to be administered to students in a high school in Mason, Ohio (Mid-West, United States). The survey questionnaire and protocol were approved by the school's Institutional Review Board (IRB). After IRB approval was obtained, consent was also sought from the school's administration. The school's administration restricted us to administer the survey to a subset of high school students. The administration did not provide an explanation for their decision to restrict the survey to only a subset of students.

Study Participants

As part of the IRB approval, the school's administration restricted us to administer the survey to a subset of students who were enrolled in the biomedical science program in the high school. As a result, the sample size ( n = 107) of our data is somewhat limited. Nevertheless, the dataset has sufficient statistical power as discussed in the results section.

Survey Administration and Data Collection

The survey was designed as a web-based (fully anonymized) survey using Google Forms ( Google Inc, 2021) to ease distribution and completion. The survey link was shared with the students with the help of teachers in the biomedical science program in the high school. We also requested the teachers to periodically remind the students to complete the voluntary survey. The fully anonymized web-based survey was made available to participants for 30 days (Dec 1 to Dec 30, 2020). Participation was voluntary and no incentives (money, prize, extra credit, etc.) were provided to the participants. The fully anonymized data was obtained from Google Forms at the end of the survey time and stored safely in a password protected CSV file for further analysis.

Data Analysis and Intermediate Variable

The survey results were obtained from Google Forms as a Comma Separated Values (CSV) file. The data was analyzed using R statistical software ( Core Team, 2020) and Microsoft Excel. As summarized in Figure 1, first we established internal consistency of the mental health estimators (dependent variables) using Cronbach's alpha. Once we established good internal consistency, we tried to reduce the number of independent variables using Principal Component Analysis (PCA) ( Jolliffe and Cadima, 2016).

To ease further analysis, we have also encoded the categorical questions to numeric values based on the Likert-like scales used for each question. In addition, to streamline analyses, we have added a dependent intermediate variable indicating change in mental health (Mt.Health.Change), which is computed as shown in the equation below.

Results

In accordance with the high school administration's conditions, the fully anonymous survey was made available to high school students (age 15-17) enrolled in the biomedical sciences program. The survey was made available for 30 days (from Dec 1-Dec 30, 2020). The voluntary survey was completed by 107 students whose demographics are summarized in Figure 2. The respondents were mostly Asian students (69 of 107 ∼ = 64%) and most of the students were female (75 of 107 ∼ = 70%). At the time of the survey, 80 of 107 participants (about 74.8%) were taking classes in-person while 27 participants (about 25.2%) were taking classes online. Out of the 107 participants, 102 participants were learning in their modality of choice (i.e., in-person or online) and had not recently switched learning modality. Four participants were considering switching learning modalities and only one participant had switched from online to in-person schooling.

FIGURE 2. Demographic summary of participants (N = 107) that completed our survey.

Our survey questionnaire requested time (in hours) spent on several key activities related to schooling each day, including: time spent in school (online or in-person), average time spent on homework, approximate time spent in exercising or sports, and average sleeping time. We solicited this data because time pressure was reported as a key stressor during our focus group discussions. In our study we aimed to delve into specific categories of time use to identify the most influential time-related stressor, if any. The summary statistics for the time usage is shown in Figure 3. Only 14 out of the 107 students (i.e., ∼13%) were not involved in a student club or an honors society.

FIGURE 3. Time distribution reported by participants for key daily activities.

Analysis of Cronbach's Alpha

Prior to statistical analysis, the internal consistency, validity and reliability of mental health estimators (dependent variables) in the survey data was verified and using the well-established Cronbach's alpha ( Taber, 2018). Cronbach's alpha requires that the data must all be on a consistent scale. Hence, for our alpha calculations, we have used the mental health change ( Eq. 1) intermediate variable along with the estimators "Increased anxiety", "Increased stress", "Issues with daily tasks?", and "Impact on relationships?" from Table 1. For the mental health estimators, the Cronbach's alpha was α = 0.78, which in social sciences is considered a good value ( Taber, 2018). Other investigations related to COVID-19 that used other instruments have reported comparable scores for Cronbach's alpha: 0.79 for GAD-7 ( Liang et al., 2020), 0.81 for GAD-7 ( Giannopoulou et al., 2021), and 0.79 for GHQ-12 ( Liang et al., 2020) to mention a few ( Tavakol and Dennick, 2011). The results from our Cronbach's alpha establish consistency, validity, and reliability of the responses. In other words, the α = 0.78, shows that the mental health assessment instrument was well designed, is unidimensional (i.e., focusing on mental health), and the responses are meaningful (i.e., students were not randomly or nonchalantly filling-in the survey, but were responding conscientiously).

T-Test for Degradation in Mental Health

An overall key question in this survey was to determine if students experienced any degradation in their mental health. In our survey, 24 out of 31 (77.4%) of White students and 37 out of 69 (53.6%) Asian students, two of 2 (100%) Hispanic/Latino, and one of 2 (50%) Black participants reported some degradation in mental health. Figure 4 shows a comparison of the distributions of mental health status reported by the participants before and during the COVID-19 pandemic. The boxplots in Figure 4B show a comparison of the mental health ratings. We conducted a paired two-sample t-test comparing the mental health of participants before and during the pandemic (see Table 1). We also verified that the change in mental health was normally distributed (a prerequisite for the t-test) using the Shapiro-Wilk normality test (W = 0.944, p-value = 0.00025). The t-test showed a statistically significant mental health degradation of 0.636 ( p ≪ 0.001). Of the 64 participants who reported degradation in mental health during the pandemic, 45 (70%) of them were enrolled in online schooling. The proportions of males and females reporting mental health degradation was 18 (28%) and 45 (70%) respectively. Amongst the 64 participants, 24 were White and 37 were Asian. Therapy was followed by nine and six participants who reported degradation and no degradation respectively.

FIGURE 4. Mental health ratings reported on a 5-point Likert-scale by the survey respondents before and during the COVID-19 pandemic. Paired t-test showed a statistically significant degradation (the changes were normally distributed -- Shapiro-Wilk test W = 0.944, p-value = 0.00025) in mental health ratings with t = 0.636 ( p ≪ 0.001). (A) Histogram of Mental Health ratings before and during the pandemic. (B) Box plots comparing the ratings before and during the pandemic.

Principal Component Analysis

Having established a good value of Cronbach's alpha for mental health estimators, we focused on analysis of stressors (independent variables) and controls (pre-existing conditions). This part of the analysis aimed to identify influential parameters and reduce the number of parameters to be addressed in further analyses. Accordingly, we have used the widely used Principal Component Analysis (PCA) ( Jolliffe and Cadima, 2016), to explore the feasibility of dimensionality reduction - i.e., check if some of the controls and stressors are redundant and can be discounted in further analyses. The key results from PCA are summarized in Figure 5. The Scree plot in Figure 5A shows that all of the parameters need to be included in order to account for 95% of the variance in the data. The monoplot also shows that all of the parameters have substantial contribution to the first two dimensions in PCA. In other words, the PCA results showed that our survey has very desirable features in that all of the stressors (independent variables) and controls are relatively uncorrelated and provide statistically significant information regarding each participant.

FIGURE 5. Results from Principal Component Analysis (PCA). (A) Scree Plot from PCA. (B) Monoplot from PCA.

Correlation Analysis

Since PCA showed that all of the stressors (independent variables) and controls (pre-existing conditions) cannot be reduced, we pursued extensive correlation analysis to estimate their influence on mental health estimators (dependent variables). The analysis was conducted using R statistical software. Figure 6 shows a subset of results from correlation analysis with stressors (independent variables) and controls (pre-existing conditions) that showed statistically significant correlation with "Mt.Health.Change" intermediate variable ( Eq. 1). The full correlogram with all stressors (independent variables), controls (pre-existing conditions), and mental health estimators (dependent variables) is included in the supplements. The upper right triangle of the correlogram shows the Spearman correlation values. Interestingly, for this data set, the Pearson correlation coefficients were exactly the same as Spearman coefficients, indicating that there is both a monotonic and linear relationships between the parameters.

FIGURE 6. Key subset of results from correlation analysis. The values in the upper-right triangle show the Spearman correlation between pairs of values. For example, 0.19 ( p = 0.049) shows the correlation between Race (the row) and Mt.Health.Change (column). The red lines in the scatter plots show the Lowess curve to highlight correlation patterns within sub-ranges of data. The full correlogram with all stressors (independent variables), controls (pre-existing conditions), and mental health estimators (dependent variables) is included in the supplementary materials.

The key intermediate variable used for analysis was "MT.Health.Change", which shows change in mental health. The correlation analysis showed that the controls (i.e., pre-existing mental health issues) had no correlation ( p > 0.1) to change in mental health. Among the various stressors, only "In-person vs. Online" learning modality and race showed statistically significant correlation r = -0.20 ( p = 0.034) and r = 0.19 ( p = 0.049) to mental health degradation. That is, students learning online reported a statistically significant degradation in mental health. A large fraction (77.4%) of White participants reported some mental health degradation, and hence some correlation with race was observed. Sleeping time (Sleep.Time) and exercise Time (PT.Time) stressors showed no statistically significant correlation to change in mental health. However, these two stressors had a good negative correlation to increase in anxiety and stress -- that is, participants who spent more time sleeping or exercising reported lower stress and anxiety changes. These observations regarding the benefits of sleep and exercise are consistent with findings reported by other investigators ( Liang et al., 2020; Giannopoulou et al., 2021).

Multivariate Regression Analysis

The correlation analysis showed that pandemic-time degradation in mental health (i.e., the Mt.Health.Change intermediate variable) is correlated only to learning modality (In-person vs. online) and the race of students. Other stressors did not show any influence on changes in mental health during the pandemic. We have cross validated this inference by identifying the best-fitting generalized regression model that explains the observations in mental health changes during the COVID-19 pandemic. For this analysis we have removed one outlier participant who was taking most of classes via the College Credit Plus (CCP) program. The best fitting regression model ( Johnson and Wichern, 2015) was determined using R statistical software which identifies parameters that minimize the Akaike Information Criterion (AIC) ( Rabbi et al., 2020). The model automatically identified included a School.Time parameter that was not significant ( p >> 0.05). When we removed it from the regression model, the Switched parameter was no longer significant ( p >> 0.05). Hence, these two parameters have been removed from our final regression model as they were not statistically significant. The results from multivariate regression analysis using the best fitting model are shown in Figure 7.

FIGURE 7. Regression results from best fitting generalized regression model. The best fitting regression model was determined using R statistical software which identifies parameters that minimize AIC.

Consistent with the correlation analysis, changes in mental health is best explained by online learning and race. Online learner's mental health degraded by about −0.9 (on a 5-point Likert scale) ( p = 0.004) while Asian participants reported lower degradation of 0.89 ( p = 0.003) when compared to other races. Exercise time (PT.Time) had only mild protective influence of 0.15 ( p = 0.037) on the mental health of participants. This is corroborated by the correlation analysis ( Figure 6) where PT.Time and Therapy was only correlated to stress and anxiety levels, but not on changes in mental health. Controlling for gender did not have any impact ( p-value >> 0.05) showing that mental health issues were equally experienced by all of the genders in our survey.

Our regression analysis identified three explanatory parameters, namely learning modality (in-person vs. online), race, and PT.Time. The first two parameters were also highlighted by correlation analysis. In order to assess the influence of PT.Time, we compared the explanatory power with and without PT.Time. We also chose this minimal subset to avoid overfitting our regression model to the survey data. Figure 8 shows a comparison of the predictions of the full regression model vs. the simpler model. Note that, in both the models, we are attempting only to predict degradation (i.e., Mt.Health.Change <0), if any, and not the magnitude of degradation.

FIGURE 8. Confusion matrix to compare the effectiveness of a regression model using just two explanatory variables, namely: learning-modality (In-person vs. online) and Race. (A) Confusion matrix with 4 factors: Learning modality, Race, Therapy, and PT. Time. (B) Confusion matrix with just 2 factors: Learning modality and Race.

The confusion matrix shows that overall, this simple model with the two explanatory variables (see Table 1 for full list) can predict potential mental health issues with an accuracy 62.6% ( p = 0.003). On the other hand, inclusion of PT.Time in the regression model improves the accuracy to 72.0% ( p << 0.001) by primarily decreasing the number of false positives (FP) from 40 to 25.

Biases and Limitations of Our Study

The biomedical science program is an elective program in the high school. Hence, the proportion of students in this program is different and hence the demographics of the survey was different than that of the high school. The percentage of male vs. female respondents was ∼27 and ∼70% respectively, while the overall high school's proportions are 51 and 49% ( US News, 2021). Similarly, the proportion of Asian (including S. E. Asian) students who responded to our survey are much higher at ∼64% whereas the overall high school's proportions is 20% Asians. This skew introduces a bias in our study. The survey did not track students who had access to the survey but chose not to participate -- prior investigations report a "pre-existing interest" bias, where participants with interest in mental health issues are more inclined to respond, which can introduce bias in the data. However, Giannopoulou et al. (2020) report that such "pre-existing interest" bias applies to most mental health surveys of COVID-19, which have mostly used web-based convenience samples ( Pierce et al., 2020). The survey then included questions on mental health estimators to assess the mental wellbeing before and during the pandemic. The survey was administered during the pandemic and hence, the question on mental health prior to the pandemic is subject to recall bias, similar to other investigations ( Giannopoulou et al., 2021). Although our study included freeform comments, we did not specifically ask participants to list aspects that cause mental health issues. Hence, our study does not draw inferences on additional factors such as family situation, social isolation, etc. ( APA 2020; Tandon 2020; Wang et al., 2020) that may degrade mental health. Lastly, the mental health estimators used in this study, despite its good internal consistency, is not a well-established instrument and hence may have an impact on the results from this study.

Discussions and Conclusions

The ongoing COVID-19 pandemic has not only caused considerable socioeconomic impacts but has also caused mental health degradation worldwide. The American Psychological Association (APA) reports that nearly 81% of Gen Z teens Gen Z teens (ages 13-17) experience more intense stress due to COVID-19, associated with schooling. Several recent investigations have confirmed the technological challenges and psychological impacts due to the COVID-19 pandemic on school students in different countries. However, similar to the APA's report, the recent studies have not focused on eliciting key stressors that cause mental health degradation in school students.

This study pursued a data-driven, scientific, and ethical approach to assess the impact of COVID-19 pandemic on the mental health of high schoolers. There are a few distinguishing aspects of this study. First, it used a focus-group based approach to elicit key stressors that impact the population under study. We found this approach to be very useful particularly due to the paucity of articles that focus on the impact of the pandemic on the high school population. Similar to recent publications to assess stress and anxiety changes, our study also used 5-questions on a 5-point Likert-like scale to elicit the mental health of the participants. The results from Cronbach's alpha (α = 0.78) analysis showed good internal consistency of our instrument. Our alpha value (α = 0.78) is considered a good value ( Taber, 2018) and is consistent with the values reported by recent investigations conducted using different assessment instruments. PCA was used to establish that the controls and stressors (explanatory/independent variables) contain important information about this survey population, adding further credence to the survey used in this study.

Our fully anonymous survey was made available via a Google Form to students enrolled in the Biomedical science program in William Mason Highschool (Ohio, United States). The school administration restricted the survey to this subset of students. No incentives of any kind were provided to the participants. Yet, a majority of the students in the program completed the survey over a period of 30 days. This outcome suggests that students are open to sharing information about their wellbeing.

A comparison of mental health ratings showed a statistically significant mental health degradation t = 0.636 ( p ≪ 0.001) before and during the pandemic as summarized in Figure 5. Correlation analysis ( Figure 6) and regression analysis ( Figure 7) showed that the most influential factor was Online schooling (estimate = −0.90), followed by race (estimate = 0.89), and exercise time (0.15). In our study, Asian students reported a much lower rate of mental health degradation. This could possibly be due to bias in our survey population because 64% of the respondents were of Asian descent. However, despite 70% of the respondents being female, no differences were found when controlling for gender, suggesting that both males and females experienced comparable mental health issues. Other factors such as pre-existing mental health issues and therapy did not influence mental health degradation.

The survey gathered time spent by the participants on several daily activities, namely: school time, homework time, exercise time, and sleeping time. We used an unpaired t-test to compare these four times reported by participants who did and did not experience mental health degradation. None of the times in the four categories showed any statistical difference. Prior to the unpaired t-test, we verified that all of the times in the four categories were normally distributed, verified using the Shapiro-Wilk normality test. In addition, except for homework time, they all had similar variances, verified using F-test.

Nevertheless, exercise time (PT.Time) was found to have explanatory power, in addition to learning-modality and race, in multivariate regression analyses. Consequently, we analyzed its contribution by comparing it with a regression model without it just two explanatory variables, namely learning-modality (In-person vs. online) and race. The simpler model was able to predict potential degradation in mental health with an accuracy of 62.3% ( p = 0.014) vs. 73.6% with PT.Time included (see Figure 8). When compared to the full regression model, the false positive rate for the simpler model is higher. The key difference between the two models is attributed to the known benefits of exercise time (PT.Time). This finding is consistent with results reported by recent investigations ( Liang et al., 2020). Adding PT.Time to the model essentially reduced the number of false positives from 40 to 25. That is, the simpler model tends to be more cautious and predict potential mental health degradation, which in this context, may be an acceptable risk. On the other hand, the number of false negatives from the simpler model is zero, showing that the chances we would miss a potential mental health problem is rather low, which is a positive aspect of the simpler model. The overall good prediction rates suggests that the two explanatory variables, namely learning-modality, and race are strong indicators of mental health changes in our survey data, with PT.Time helping to mitigate impact of stressors on mental health.

Multiple statistical analyses clearly showed degradation in mental health of students pursuing online schooling. Several recent articles suggest that this degradation can be attributed to a feeling of social isolation, loss of social structure in the form of friends or clubs (85% of students reported being in a club or student organizations), and family issues ( Zhang et al., 2020). Although, our questionnaire did not specifically solicit factors impacting mental health, 45.8% of the participants provided freeform comments. Reviewing the freeform feedback identified three recurring themes in the feedback. First, 14 participants (13.2%) indicated that stress caused by homework as being an influential factor and suggested coordination between instructors to spread the workload. Second, nine participants (8.5%) reported social isolation or lack of social interactions as being a factor. Third, 13 participants (12.3%) suggest increasing community support and social interactions through flexible times or during short breaks during school time could improve mental wellbeing.

The regression analysis showed that exercise has a positive impact on mental wellbeing. Hence, online schooling can explore opportunities to engage students by providing breaks for physical activity. Electronic approaches for gaming such as Wii exercise could be used for exercising and their results can be used to provide bonus points for students to encourage regular exercising. Exercises can be combined with the aforementioned actionable approaches proposed by participants to further alleviate some of the stressors experienced by high schoolers.

Combining with the data published by the APA (2020), this study further helps our community to move from correlation to causality, which is a critical step in addressing the rising amount of mental health issues in younger generations in the United States. Although our results are subject to some bias in race, there is no gender bias. Hence, our results are also applicable in other communities and possibly in other regions as well. This study provides survey instruments and valuable inferences to many communities to help improve mental health of their children. By conducting further research, the scientific community would be able to learn more about what impacts mental health and how to help those in need.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://pc2lab.cec.miamioh.edu/documents/RawMentalHealthSurvey.xlsx.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Review Board, William Mason High School, Mason, OHIO, United States. Written informed consent from the participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author Contributions

MR conceived the research, designed the study, developed survey, collected data, helped with data analysis, helped with creating charts and figures, and writing the article. DR helped with data analysis, drawing inferences, assisted with creating charts and figures, and writing the article.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank Susana Maldonado and Victoria Rao for their continuous encouragement and support.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2021.719539/full#supplementary-material

References

Core Team (2020). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at:.

Johnson, R. A., and Wichern, D. (2015). "Multivariate Analysis," in Wiley StatsRef: Statistics Reference Online (John Wiley & Sons, Inc.), 1-20. doi:10.1002/9781118445112.stat02623.pub2

CrossRef Full Text | Google Scholar

U.S. News (2012). William Mason High School. Retrieved from:

# data[].attributes.attachment_link.title
The Mental Health of High School Students During the COVID-19 Pandemic

# data[].attributes.attachment_link.description
The American Psychological Association (APA) reports 81% of Gen Z teens (ages 13-17) have experienced more intense stress during the COVID-19 pandemic. This study uses a survey-based approach along with robust statistical analyses to identify key stressors from a set of students in a high school in Midwest United States.

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As adults, we all have diverse needs and different ways of coping with stressful situations. For children, it is the same. Some children will have dealt well with restrictions and school closures caused by the COVID-19 pandemic. For others, it is challenging to cope with all the changes and uncertainty. Some children will return to school having experienced some level of stress, anxiety, isolation and grief. Some may have experienced increased violence at home.

School teachers and personnel are critical in supporting children's transition back to in-person classroom learning, particularly after extended periods of school closure. In addition to continuing to use the different skills teachers have been using to ensure their students' learning and emotional well-being while schools were closed, the following suggestions might be helpful when schools reopen:

1. Listen to children's concerns

COVID-19 and school closures impacted many children and adolescents' mental health and well-being. As teachers, it is essential to listen to students' concerns and demonstrate understanding as well as empathy. Offer your students the opportunity to have a one-to-one conversation with you to reconnect and discuss any concerns that might have arisen when their school was closed. If a child shares anything that is particularly concerning, please follow the protection or child safeguarding systems in place.

2. Check how children are doing

Before teaching new academic content to students, teachers and school personnel should take time to check how children are doing. Remember that children may have difficulty concentrating at first or may need more time to get back into the routine of learning. Provide opportunities for children to take breaks, move around, and re-connect with their friends and peers.

3. Provide children with accurate information around COVID-19

As children return to school, they may have different thoughts and questions about COVID-19. Children want and need factual information. Use child-friendly and age-appropriate resources available in your country/region that is based on scientific evidence to respond to children's questions about COVID-19 accurately.

While it is important to acknowledge the scale of what is happening globally, make sure to emphasize all the efforts made and precautions taken to reduce risks in the school reopening plans. Do not forget to remind children of school safety protocols, including what to do in case there is a COVID-19 case detected in the classroom.

4. Seek suggestions from children on how to create a welcoming, safe and comfortable classroom

Engage children in making the classroom a welcoming, safe and comfortable space. When doing so, make sure to respect school safety procedures and use available material resources.

Children can provide suggestions; help decorate the walls of the classroom with colourful and welcoming messages and work in small groups so they can support each other to catch up on learning. Let them know that being supportive of each other will help them get through this together. Remember to praise children for their contributions and efforts. Teachers can foster feelings of safety and security by interacting and developing positive relationships with each student and using routines during the day to help children feel safe and secure.

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8 ways teachers can support students' mental health during COVID-19 school returns

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Transitioning back to in-person classroom learning during COVID-19

# data[].attributes.headline
Promoting mental health and wellbeing in your school

# data[].attributes.subject
Promoting mental health and wellbeing in your school

# data[].attributes.body
https://www.education.vic.gov.au/school/teachers/health/mentalhealth/Pages/promoting-mental-health.aspx

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Schools also play a role in creating a positive community that enhances a sense of connection and belonging in students. Using a whole school approach, schools can develop a safe and inclusive school environment and promote student mental health and wellbeing. This is best achieved by working in partnership with all members of the school community.

Schools can support students to develop:

social and emotional skills and capabilities that promote life-long mental health and wellbeing

skills to problem-solve and manage the normal stressors of life

healthy relationships and the ability to work cooperatively with others

a sense of autonomy and purpose.

The Schools Mental Health Menu is a list of endorsed programs, staff and resources across 3 tiers of support.

Tier 1 activities form a foundation for Tier 2 and Tier 3. Together, the tiers form layers of support for students. Tier 1 supports remain in place, and schools can add extra supports from Tier 2 and Tier 3 when greater needs are identified.

Tier 1: Universal interventions

Tier 1 Universal interventions help schools to create a positive, inclusive and supportive school climate, building the preconditions for optimal student learning, development and wellbeing. They are the initiatives and programs schools adopt that are good for all students.

Activities can be implemented at a school, year, or classroom level. They are usually delivered by school staff. Community organisations or agencies may be employed to deliver some activities.

Programs include:

adopting a whole school approach to mental health

social and emotional learning

wellbeing animal programs

eating disorders supports

alcohol and drug education

mental health literacy

bullying prevention

local community lead mental health intervention

peer support.

The Schools Mental Health Menu provides a comprehensive information about tier 1 activities (as well as tiers 2 and 3) available to schools.

# data[].attributes.attachment_link.title
School's role in Tier 1 promoting mental health and wellbeing | Victorian Government

# data[].attributes.attachment_link.description
Schools also play a role in creating a positive community that enhances a sense of connection and belonging in students. Using a whole school approach, schools can develop a safe and inclusive school environment and promote student mental health and wellbeing. This is best achieved by working in partnership with all members of the school community.

# data[].attributes.attachment_link.readable_text
Schools also play a role in creating a positive community that enhances a sense of connection and belonging in students. Using a whole school approach, schools can develop a safe and inclusive school environment and promote student mental health and wellbeing. This is best achieved by working in partnership with all members of the school community.

Schools can support students to develop:

social and emotional skills and capabilities that promote life-long mental health and wellbeing

skills to problem-solve and manage the normal stressors of life

healthy relationships and the ability to work cooperatively with others

a sense of autonomy and purpose.

The Schools Mental Health Menu is a list of endorsed programs, staff and resources across 3 tiers of support.

Tier 1 activities form a foundation for Tier 2 and Tier 3. Together, the tiers form layers of support for students. Tier 1 supports remain in place, and schools can add extra supports from Tier 2 and Tier 3 when greater needs are identified.

Tier 1: Universal interventions

Tier 1 Universal interventions help schools to create a positive, inclusive and supportive school climate, building the preconditions for optimal student learning, development and wellbeing. They are the initiatives and programs schools adopt that are good for all students.

Activities can be implemented at a school, year, or classroom level. They are usually delivered by school staff. Community organisations or agencies may be employed to deliver some activities.

Programs include:

adopting a whole school approach to mental health

social and emotional learning

wellbeing animal programs

eating disorders supports

alcohol and drug education

mental health literacy

bullying prevention

local community lead mental health intervention

peer support.

The Schools Mental Health Menu provides a comprehensive information about tier 1 activities (as well as tiers 2 and 3) available to schools.

# data[].attributes.attachment_link.title
School's role in Tier 1 promoting mental health and wellbeing | Victorian Government

# data[].attributes.attachment_link.description
Schools also play a role in creating a positive community that enhances a sense of connection and belonging in students. Using a whole school approach, schools can develop a safe and inclusive school environment and promote student mental health and wellbeing. This is best achieved by working in partnership with all members of the school community.