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. 2026 May 12;104(49):e46751. doi: 10.1097/MD.0000000000046751

Spectrum of mental health among students: A scientometric analysis of 20 years of research on depression and anxiety

Irum Zeb a, Aashiq Khan b, Yuhao Su c,*
PMCID: PMC12688983  PMID: 41366885

Abstract

Mental health and well-being are vital for students’ success. Over the past decade, concerns about student mental health have increased globally. However, research in this field remains fragmented. This study analyzes research conducted from 2004 to 2024. The Web of Science database was used to analyze 84,024 articles to map global research trends in student mental health, with a focus on depression and anxiety. Using VOSviewer, we identified key journals such as Depression and Anxiety (210 publications, 10,747 citations), BMC Psychiatry, and the Journal of Affective Disorders as dominant publication sources. Keyword analysis highlights depression, anxiety, and prevalence as central themes. Leading researchers include Penninx B.W.J.H. with 47 publications and Kessler R.C. with 5518 citations. The most-cited study is by Kessler et al. (2012), with 1927 citations. Geographically, the United States leads in research output with 960 documents and 46,546 citations, followed by the Netherlands (321 documents) and the United Kingdom (396 documents). China ranks seventh with 213 documents, while India does not appear among the top 10 contributors. The analysis reveals limitations, such as database bias due to reliance on Web of Science and citation imbalances. We propose expanding data sources, standardizing terminology, and integrating qualitative methods. These findings emphasize the need for equitable global collaboration and policy-driven interventions to address student mental health challenges effectively.

Keywords: depression and anxiety bibliometric analysis, student mental health, VOS viewer

1. Introduction

Mental health problems have become a growing concern worldwide, with depression and anxiety being the most dominant disorders.[1,2] Students’ mental health is foundational to their ability to thrive academically, socially and in terms of their general quality of life. Anxiety and depression have attracted attention in recent years, especially among students in higher education.[3] Academic pressure has been significantly correlated with greater psychological distress, which can undermine students’ cognitive, emotional, and social competencies.[4,5] In the past decade, more students have reported experiencing symptoms of these conditions, impacting their academic performance, social relationships, and overall well-being. Approximately 31% of students screen positive for a mental health disorder worldwide.[6] The impact of mental health issues varies between traditional and nontraditional students. Traditional students, who are younger and often financially dependent on their parents, face unique stressors, such as academic pressure, adapting to independence, and cultural adjustments.[7] Nontraditional students, typically older and employed full-time, must balance work, family responsibilities, and academic demands, which can further exacerbate mental health risks.[8] Despite this increasing prevalence, many students struggle to access appropriate mental health care. A major challenge is the lack of mental health literacy, leading students to perceive symptoms of depression and anxiety as normal stress rather than conditions requiring professional intervention.[9,10] Even those who recognize their need for help often face barriers such as limited access to services, concerns about treatment effectiveness, and the perceived inconvenience of seeking care.[11] This growing crisis requires a closer look at the patterns, causes, and potential solutions regarding students’ mental health challenges. Traditional psychological and epidemiological approaches provide valuable insights into the prevalence and risk factors for anxiety and depression.[12,13] Nevertheless, these approaches often adopt a narrow approach that can obscure the bigger picture. Previous studies on student mental health and anxiety span various disciplines, as demonstrated by.[14–17] Recent scientometric studies have further mapped global research on mental health among university students.[10] Scientometric analysis, a method for systematically reviewing the academic literature, provides a means to assess trends, research collaboration, and emerging themes in studies related to mental health.[18] Countless analyses of research streams have already been conducted in academia,[19] utilizing approaches such as co-citation analysis, co-occurrence and co-word mapping, allowing scholars to trace the development of this body of research and extract findings.

While anxiety and depression are frequently discussed in tandem, they are separate conditions that have their defining features. Depression is characterized by a pervasively low mood, diminished interest in daily activities, and impaired functioning.[20] Psychological studies prioritize personal levers such as academic-related stress.[21] Scientometric analysis plays an important role in modern research by mapping cognitive and landscape overall health studies.[22–24] By analyzing research networks, citation patterns, and keyword trends, scholars can identify key contributors, emerging themes, and collaboration gaps.[25] However, a critical gap remains, and a comprehensive global scientometric analysis on this topic is lacking. This gap in research is significant. Moreover, few studies have used advanced visualization tools such as the VOS viewer to explore the relationship between depression and anxiety in student populations.[26,27] Scientometric analysis, which employs visualized statistical methods to examine scholarly literature, is instrumental in detecting key research trends and gaps. The VOS viewer was used for data analysis, facilitating the mapping of historical developments and anticipating future directions in the field. This research aims to analyze the scientific landscape of student mental health, with a particular focus on depression and anxiety, through the following research questions. To what extent has scientometric analysis been utilized to examine the spectrum of mental health, including depression and anxiety, among students in the global academic landscape? What are the dominant research trends, influential publications, and key themes emerging from scientometric studies on student mental health, particularly with respect to depression and anxiety? What gaps or underexplored areas exist within the scientometric literature on the mental health spectrum of students, specifically in relation to anxiety and depression? How can insights from scientometric analyses contribute to enhancing students’ mental health, addressing depression and anxiety, and informing policies within higher education?

2. Review strategy

This study employed a scientometric analysis of bibliographic data[28,29] to quantify various characteristics of the dataset. Scientometric studies rely on scientific mapping, a method widely used in bibliometric analysis.[30,31] Given the extensive number of publications on the subject, selecting a reliable database was essential. Web of Science (WOS) is a highly accurate and widely recommended database for this purpose.[32,33] This study used the WOS core collection as the single data source due to its rigorous indexing, multidisciplinary coverage, and compatibility with scientometric tools like VOSviewer. WOS is widely recognized for its consistent and structured metadata, which is essential for accurate bibliometric mapping and network analysis. While this may exclude some non-English or regional journals, focusing on WOS ensures high data quality, comparability, and methodological consistency.

A search conducted in WOS for the terms “mental health; depression & anxiety” from 2004 to 2024 yielded 84,024 results. To ensure the dataset focused on students, additional filtering terms such as “student,” “university student,” “college student,” and “higher education” were used. To improve comprehensiveness and avoid inappropriate exclusion, terminological variations were also considered. For example, both “depression” and “depressive disorder,” as well as “anxiety” and “generalized anxiety disorder,” were included using Boolean logic and wildcard operators where applicable. Various filters were applied to refine the dataset and exclude irrelevant publications. Figure 1 presents a flowchart detailing the data retrieval process, analysis steps, and applied filters. Similar approaches have been reported in previous studies.[34] After applying the necessary filters, 2379 relevant records remained. The selected WOS records were saved in a tab-delimited format for further analysis via specialized software. VOS viewer (version 1.6.20) were employed to generate scientific visualizations and conduct a quantitative evaluation of the data. As an open-source mapping tool widely used across various research domains, VOSviewer is recognized as a reliable software for bibliometric analysis.[35] The tab-delimited file was imported into VOS viewer, ensuring consistency and accuracy in data evaluation.

Figure 1.

Figure 1.

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The review methodology, illustrating the selected options and imposing limitations. This figure depicts the methodology used in the review process, highlighting the options selected and the limitations imposed.

In VOSviewer (version 1.6.20), the full counting method was used for co-authorship and co-occurrence analyses. Clustering was based on the LinLog/modularity algorithm. A minimum threshold of 5 items (e.g., keywords, documents, journals) was applied, and standard layout parameters (attraction = 2, repulsion = -1) were used. These settings follow established scientometric protocols to ensure clarity and reproducibility of results.[36]

The scientometric analysis examined key publishing outlets, the most frequently occurring keywords, leading researchers on the basis of publication and citation counts, highly cited documents, and contributions from different regions. The relationships between various features were visualized through mapping techniques, and the quantitative data were presented in tabular form. The color of each item on the map generated corresponds to its respective cluster. For density visualization, multiple color schemes, such as viridis, plasma, and rainbow schemes, are available; this study utilized the rainbow color scheme for density mapping.

2.1. Ethics approval

This study did not require the approval of an ethics committee since we analyzed a secondary database.

3. Results

This section presents the findings of the scientometric analysis in a structured manner, integrating visual (figures) and tabular data to illustrate disciplinary trends, publication patterns, authorship networks, keyword mapping, and geographic contributions.

3.1. Annual publication trends by subject area

As shown in Figure 2, the top 3 disciplines contributing to the dataset were psychiatry, clinical psychology, and psychology, accounting for approximately 82%, 20%, and 16% of the total documents, respectively. Together, these fields contributed 73% of all the publications. Additionally, the publication types in the selected research area were analyzed. As illustrated in Figure 3, journal articles represented most publications (91%), followed by book chapters (0.13%), early access publications (4%), conference proceedings (0.55%), review articles (4%), and other document types (8%).

Figure 2.

Figure 2.

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Distribution of mental health research by academic discipline. This figure shows the distribution of mental health research across different academic disciplines, helping to visualize the areas of focus in the field.

Figure 3.

Figure 3.

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Document types published in mental health research (2004–2024). This figure categorizes the different types of documents published in the field of mental health research over the last 2 decades.

Figure 4 presents the annual publication trends in the field of mental health from January 2004 to December 2024, demonstrating a steady increase in research output over time. Between 2004 and 2015, the number of publications remained relatively low, averaging approximately 91 articles per year. However, a significant increase was observed from 2016 onward, with an average of 152 articles published annually between 2016 and 2021. The highest publication count was recorded in 2023, reaching 267 articles, followed by 218 publications in 2024. This upward trend reflects a growing research interest in mental health, highlighting its increasing importance in academic and clinical studies.

Figure 4.

Figure 4.

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Publication year of documents on mental health research up to May 2024. This figure presents the distribution of publications in mental health research by year, highlighting trends in the volume of publications over time.

Figure 2 shows that psychiatry is the leading discipline, which means mental health issues like depression and anxiety are mostly seen from a medical and clinical perspective. Psychology and clinical psychology also contribute a lot, showing some collaboration between fields, but are still focused on health. Figure 3 shows that most studies are published as peer-reviewed journal articles, highlighting the importance of careful academic review. Fewer review articles and conference papers suggest that using a wider range of publications could help share knowledge more widely.

3.2. Bibliographic coupling of publication sources

The analysis of publication outlets (journals) was conducted via the VOS viewer tool on the basis of bibliographic data. A minimum threshold of 5 publications per source was applied, and 90 out of 298 sources met this criterion. Table 1 presents the journals that published at least 5 articles on mental health research up to December 2024, along with the number of citations received within this period. The top 3 journals in terms of publication count were Depression and Anxiety (210 articles), BMC Psychiatry (186 articles), and the Journal of Affective Disorders (157 articles). In terms of citations, the most influential sources up to December 2024 were depression and anxiety, which received 10,747 citations, followed by BMC Psychology (4611 citations) and the Journal of Affective Disorders (4578 citations). This analysis provides a foundation for future scientometric evaluations in mental health research. Unlike traditional review studies, which often lack systematic graphical representations, this study presents structured visualizations. Figure 5 shows a network visualization of sources that published at least 90 articles. In Figure 5A, the frame size represents the journal’s influence on the document count; a larger frame indicates a greater impact. Depression and anxiety have the largest frame, signifying their significant role in the mental health research field. The VOS viewer analysis grouped journals into 6 clusters, each represented by a different color (blue, red, purple, yellow, cyan, and green). These clusters are formed on the basis of the frequency of co-citations, meaning that journals frequently cited together appear in the same group.[37]

Table 1.

List of publication outlets with at least 90 publications on mental health up to December 2024.

S. no. Source Publication count Total citations received
1 Depress Anxiety 210 10,747
2 BMC Psychiatry 186 4611
3 J Affect Disord 157 4578
4 Psychol Med 91 6033
5 Front Psychiatry 97 712
6 Br J Psychiatry 57 6854
7 Can J Psychiatry-Revue Canadienne De Psychiatrie 60 3179
8 Front Psychology 49 321
9 J Anxiety Disord 32 957
10 Am J Geriatr Psychiatry 43 1729
11 Int J Methods Psychiatr Res 30 3635
12 Soc Psychiatry Psychiatr Epidemiol 32 1021
13 Gen Hosp Psychiatry 25 650
14 Health Technol Assess 12 249
15 Epidemiol Psychiatr Sci 25 1212
16 Neuropsychiatr Dis Treat 37 527
17 Psychiatry Res 30 899
18 BMC Health Serv Res 26 348
19 J Psychiatr Res 22 1618
20 Int J Mental Health 32 517
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Figure 5.

Figure 5.

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(A) Source mapping by network visualization. This figure visualizes the sources in the field of mental health research using network mapping techniques. (B) Source mapping by density. This figure illustrates the density of sources in mental health research, offering insight into how research is distributed across different sources.

The red cluster contains eleven journals that are frequently co-cited within the same research articles. Additionally, journals positioned closely within a cluster exhibit stronger connections than those further apart. BMC Psychiatry has a stronger correlation with Frontiers in Psychiatry than with European Psychiatry or the International Journal of Mental Health, as illustrated in Figure 5B. The density visualization further highlights key journals, with yellow areas indicating the highest concentration of co-citations. These high-density areas are primarily observed around BMC psychiatry and depression and anxiety, reflecting their substantial influence in the field of mental health research.

3.3. Co-occurrence of keywords

Keywords play a key role in research, as they define and highlight the core topics within a study domain.[38] In this analysis, a minimum occurrence threshold of 5 was set, resulting in the selection of 857 keywords out of 6267. Table 2 presents the top 20 keywords that are most frequently used in published studies on the topic. The 5 most commonly occurring terms are depression, anxiety, prevalence, disorders, and anxiety disorders. The keyword analysis reveals that depression is a predominant research focus, particularly in relation to mental health issues among students. This includes aspects such as academic-related anxiety, stress, depression, and workload. Figure 6 provides a structured visualization of keyword co-occurrence, connections, and density, which are proportional to their frequency. In Figure 6A, the frame size of a keyword reflects its frequency of occurrence, whereas its position indicates co-occurrence relationships within research articles. Keywords with larger frames appear more frequently and are central to mental health research. The graph in Figure 6 also illustrates clusters based on keyword co-occurrence across various publications. The color-coded clusters signify how frequently specific keywords appear together. In Figure 6A, 5 distinct clusters are represented by different colors. Figure 6B further visualizes keyword density, with varying shades indicating different concentration levels. The bright yellow areas represent the highest keyword density, whereas the dark blue areas indicate the lowest. High-density yellow areas center around depression, showing strong connections with terms such as comorbidity, major depressive disorder, and cognitive–behavioral therapy. The moderate-density green areas included terms such as panic disorder, social phobia, stress, and adolescent mental health, indicating moderate co-occurrence. Low-density dark blue areas feature less frequently used keywords such as randomized trials, quality of care, and serotonin reuptake inhibitors, suggesting peripheral relevance. These findings can help researchers choose the right keywords to find published papers on a specific topic easily.

Table 2.

List of the 20 most used keywords in mental health research.

S. no. Keyword Occurrences
1 Depression 1355
2 Anxiety 955
3 Prevalence 568
4 Disorders 396
5 Anxiety disorders 318
6 Major depression 310
7 Comorbidity 290
8 Mental health 245
9 Symptoms 235
10 Primary care 224
11 Validation 214
12 Epidemiology 208
13 Validity 207
14 Mental health 214
15 Health 192
16 Meta analysis 175
17 Major depressive disorder 179
18 Panic disorder 165
19 Primary care 157
20 Mental disorders 166
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Figure 6.

Figure 6.

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(A) Keywords Co-occurrence Scientific Mapping. This figure displays the co-occurrence of key terms in mental health research, showing the relationships between frequently used keywords. (B) Keywords Co-occurrence Density. This figure represents the density of co-occurring keywords in mental health research, helping to identify the most connected terms.

3.4. Authors’ coauthor ship

Citations reflect a researcher’s impact in a specific field of study.[39] In this analysis, a minimum threshold of 5 publications per researcher was set, with 283 out of 10,898 researchers meeting this criterion. Table 3 lists the most prolific authors, and their citation counts in the field of mental health, as determined via the VOS viewer tool. The average number of citations per author was calculated by dividing the total number of citations by the total number of articles published. Evaluating a researcher’s overall effectiveness can be complex, as it involves multiple factors, including the number of publications, total citations, and average citations per article. To simplify the analysis, rankings can be examined separately for each metric. The study identified Penninx B.W.J.H. as the most productive author, with 47 publications, followed by Cuijpers P., with 41 publications, and Craske M.G., with 34 publications. In terms of total citations, Kessler R.C. ranked highest with 5518 citations, followed by Cuijpers P. with 4207 citations and Penninx B.W.J.H. with 2658 citations. When considering the average number of citations per article, Penninx B.W.J.H. and Craske M.G. led with approximately 163 and 162 citations per article, respectively, whereas Stein M.B. followed with an average of 135 citations per article. Figure 7 visualizes the network of co-authorship among researchers significantly contributing to mental health research. The first visualization represents a co-authorship network, highlighting authors who have published at least 15 papers in the field. Different clusters indicate groups of researchers who frequently collaborate, with stronger connections signifying more frequent co-authorships. The second visualization provides a density representation of the co-authorship network, identifying the most influential authors on the basis of citation impact. The bright yellow areas indicate the regions with the highest concentration of highly cited researchers. This analysis confirms that mental health researchers are highly interconnected through citations, forming a strong academic network that supports knowledge dissemination and collaboration in the field.

Table 3.

Lists of scholars with at least 15 publications on mental health up to December 2024.

S. no. Author Published articles Citations Average citations count
1 Penninx, Brenda W. J. H. 47 2658 163
2 Cuijpers, Pim 41 4207 103
3 Craske, Michelle G. 34 2062 162
4 Stein, Murray B. 28 1258 135
5 Van Marwijk, Harm W. J. 27 1893 95
6 Kessler, Ronald C. 25 5518 44
7 Beekman, Aartjan T. F. 24 2161 108
8 Rush, A. John 22 882 74
9 Schoevers, Robert A. 22 799 65
10 Reynolds, Charles F., Iii 22 1110 54
11 Trivedi, Madhukar H. 20 674 64
12 Wetherell, Julie Loebach 19 2118 45
13 De Graaf, Ron 18 1501 74
14 Fava, Maurizio 18 517 58
15 Sullivan, Greer 17 708 111
16 Lewis, Glyn 17 376 71
17 Bystritsky, Alexander 16 543 100
18 Lenze, Eric J. 16 1125 49
19 Vasiliadis, Helen-Maria 16 170 37
20 Batelaan, Neeltje M. 15 386 42
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Figure 7.

Figure 7.

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(A) Science mapping of authors with at least 15 publications. This figure maps authors who have at least 15 publications in the field of mental health research. (B) Science Mapping of Connected Authors Based on Citations. This figure shows a network map of authors connected by citations, highlighting collaborative research trends.

3.5. Bibliographic coupling of documents

The number of citations a publication receives reflects its impact within a specific research field. Highly cited papers are often considered pioneering contributions in their respective domains. In this study, a minimum threshold of 5 citations per document was set, with 2329 publications meeting this criterion. Table 4 lists the top 5 most-cited articles in the field of mental health, along with their respective authors and citation counts. The most influential work, “Risk of Anxiety and Mood Disorders in the United States” by,[40] has received 1927 citations. Other highly cited works include,[41] with 1432 citations, and,[42] with 1132 citations. Notably, as of December 2024, only 5 articles have accumulated more than 14,500 citations. Figure 8 illustrates the scientific visualization of interconnected articles based on citation links and density concentration within the current study domain. Figure 8A shows that 131 out of 80 publications were linked through bibliographic coupling, as analyzed via the VOS viewer. Figure 8B presents a density map, highlighting the areas with the highest citation concentration, indicating the most influential articles in the field. This analysis highlights key research contributions in mental health and provides a foundation for future research by identifying high-impact studies that shape the field.

Table 4.

List of the top 5 articles in terms of citations received up to December 2024.

S. no Article Title Citations received
1 (Kessler et al, 2012) Risk of anxiety and mood disorders in the United States 1927
2 (Fiske et al, 2009) Depression in older adults 1432
3 (Kessler et al, 2009) The global burden of mental disorders 1132
4 (Hofmann and Smits, 2008) Cognitive–behavioral therapy for adult anxiety disorders 1008
5 (Wittchen et al, 2011) The size and burden of mental disorders and other 991
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Figure 8.

Figure 8.

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(A) Systematic map of published documents linked by citations. This figure illustrates a systematic map of documents in mental health research, showing how publications are linked through citations. (B) Systematic map of published documents with connected document density. This figure depicts the density of linked documents in the field of mental health research, offering insights into the interconnectedness of studies.

3.6. Bibliographic coupling of countries

Different countries have contributed varying numbers of documents to the field of mental health research, with some nations making significant contributions and continuing to do so. To visualize these contributions, a systematic map was created, allowing readers to examine regions actively engaged in mental health research. For this analysis, a minimum threshold of 5 documents per country was set, with 109 documents per nation as the requirement. A total of 67 countries met these criteria. Table 5 presents the countries with the most substantial research output, highlighting the number of publications, total citations received, total link strength, and citations per document. The United States leads with 960 publications and 46,546 citations, averaging 48.4 citations per document. The Netherlands follows with 321 documents and 17,263 citations, 53.8 citations per document, while UK ranks third with 396 documents and 14,614 citations, 36.9 citations per document. Notably, Germany 64.6, Italy 65.8, and Spain 59.5 demonstrate high citation impact relative to their document count, reflecting substantial scholarly influence despite publishing fewer articles than the leading contributors. The U.S. produces the most research and has the highest impact overall, but some European countries like the Netherlands and Germany have more citations on average for each paper. This means their publications may have more influence. For example, China publishes many papers (213), but each paper gets fewer citations (32.2 on average) compared to countries like Italy (65.8) and Germany (64.6), showing less global impact per publication.

Table 5.

Bibliographic coupling of countries.

S.No Country Documents Citations Citations per document Total link strength
1 USA 960 46,546 48.4 746,620
2 Netherlands 321 17,263 53.8 357,405
3 UK 396 14,614 36.9 355,592
4 Canada 242 8325 34.4 242,865
5 Australia 241 10,842 45 240,015
6 Spain 142 8446 59.5 206,626
7 Peoples R China 213 6864 32.2 200,852
8 Germany 168 10,852 64.6 198,838
9 Italy 88 5787 65.8 143,044
10 Japan 68 3881 57.1 111,198
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Figure 9A presents a network map that highlights the United States as the most influential country, showing strong research connections UK, Canada, Germany, and Australia. Countries are grouped into clusters since citation similarity, with major research hubs identified in North America, Europe, and Asia. Figure 9B further reinforces these findings through a density map, indicating high research activity in the United States, followed by UK, Germany, and the Netherlands. This graphical representation, combined with the quantitative analysis of participating nations, provides valuable insights for early-career researchers. It can help them establish scientific collaborations, initiate joint research projects, and share innovative research methodologies. Additionally, scholars from developing research nations can collaborate with experts in well-established countries, benefiting from their expertise and resources.

Figure 9.

Figure 9.

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(A) Science mapping of countries by network map. This figure visualizes countries in mental health research based on network mapping, showing the global distribution of research. (B) Science mapping of countries by density. This figure presents the density of mental health research by country, highlighting the regions most active in the field.

4. Discussion

This systematic review conducted a statistical analysis and mapping of bibliographic data available in mental health research. Previous manual review studies lacked the capacity to fully and accurately link different areas of literature. This analysis identified the sources of publications (journals) that published the greatest number of research documents, the most frequently used keywords in publications, the most highly cited documents and researchers, and the countries actively contributing to mental health research. According to the assessment of keywords, depression and anxiety have been the most extensively studied aspects of student mental health. The scientometric analysis revealed major trends in mental health research by identifying key contributors and their impact. Furthermore, the analysis highlighted the connections between various studies through citation networks, providing insights into the evolution of research in this field. The mapping of literature and its citation linkages helped to determine the most engaged and influential nations on the basis of their research output. The United States leads in both the number of studies and citations, likely because of several reasons. These include strong government funding, good research facilities, and wide international collaborations. Previous studies show that U.S. universities receive steady support for mental health research from organizations like the National Institutes of Health, which funds large projects and teamwork across different fields.[43,44]

Additionally, the graphical representation and quantitative analysis of the participating countries and researchers offer valuable insights for young scientists. These insights can facilitate the formation of scientific collaborations, the establishment of joint research ventures, and the exchange of advanced research methodologies and concepts. Scholars from countries aiming to expand research on student mental health can collaborate with experienced professionals in the field and gain from their expertise. It is also important to understand why some countries, like China and India, have lower visibility in mental health research. China is ranked seventh in the number of publications, but its average citations are low. This could be due to challenges in publishing in top international journals, language barriers, and fewer global research collaborations.[45] India does not appear in the top ten, which may be because of different national research priorities, low funding for mental health, and problems within its academic publishing system[46] To reduce these gaps, countries need better policies, more funding, and support for international partnerships and open-access publishing.

5. Applications for scientometric analysis in mental health research

Scientometric analysis is a valuable tool for mapping research trends in mental health, particularly depression and anxiety, among students. It identifies key contributors, publication sources, and thematic developments over the past 2 decades.[47] By using bibliometric techniques such as co-occurrence mapping and citation network analysis, researchers can trace intellectual progress and uncover emerging areas of interest.[19,48] This approach benefits universities, policymakers, and funding agencies by guiding mental health support programs and resource allocation on the basis of empirical evidence.[49] It also highlights international collaborations, fostering global partnerships and mentorship opportunities.[50] Additionally, bibliometric mapping has been applied to specific populations, such as patients with oral cancers, illustrating the growing clinical relevance of mental health research.[51] AI-driven tools such as the VOS viewer and CiteSpace enable real-time tracking of research developments, helping identify gaps and predict future directions.[52] However, challenges such as database limitations and keyword variability must be addressed to increase the accuracy and comprehensiveness of scientometric analysis.[53] As mental health remains a global priority, leveraging scientometric insights can improve research impact, collaboration, and intervention frameworks, ultimately enhancing student well-being worldwide.

6. Limitations and potential remedies

Scientometric analysis has advanced mental health research but faces limitations that affect its accuracy and applicability. A key issue is its reliance on databases such as WOS and Scopus, which may exclude regional or nonindexed journals, leading to geographic and disciplinary biases. Expanding data sources to include platforms such as Google Scholar and PubMed can improve coverage.[18,22] Additionally, variations in terminology and evolving trends can hinder consistent data retrieval. Standardized thesauri and natural language processing techniques can refine keyword searches and enhance inclusivity.[25] Citation metrics, often used to measure impact, can be skewed by self-citations and clustering, favoring dominant researchers. Network-based normalization and limiting self-citations can address these biases.[54] Furthermore, scientometric analysis prioritizes publication metrics over research quality, potentially overlooking methodological rigor. Combining qualitative reviews with bibliometric methods can provide a more balanced evaluation.[55] Socioeconomic and cultural factors, which significantly influence mental health research, are often underrepresented. Promoting open-access publishing and integrating policy analyses can improve visibility for studies from low-income regions.[56] While co-authorship networks are mapped effectively, they may not reflect the depth of collaboration. Qualitative assessments can help distinguish formal partnerships from substantive partnerships.[57] Finally, technological limitations in tools such as the VOS viewer and CiteSpace can affect data accuracy. Advancements in AI and machine learning can enhance analysis, enabling more precise trend predictions and thematic insights.[58] Addressing these challenges through interdisciplinary approaches and methodological improvements will strengthen the reliability and utility of scientometric analysis in mental health research.

7. Conclusion

This study conducted a scientometric analysis of 84,024 articles from the WOS database to assess research on student mental health, focusing on depression and anxiety. Key journals such as Depression and Anxiety, BMC Psychiatry, and the Journal of Affective Disorders dominated the field. Leading researchers include Penninx B.W.J.H. and Kessler R.C., whose work significantly shapes the field. Geographically, the United States leads in research output, but countries such as China and India show lower visibility, with China ranking seventh and India absent from the top ten. The analysis identified several key trends and limitations. For example, the low contribution rates from certain countries, particularly from developing regions like India and some parts of Asia, may reflect structural barriers such as limited research funding, access to international journals, and language challenges. To address these issues, we recommend enhancing academic exchange programs and fostering collaborative research initiatives between high-output countries (e.g., the U.S.) and developing nations. These programs could help bridge the gap in research output and citation impact. Additionally, promoting open-access policies and funding for research in underserved regions could increase the visibility and impact of studies from these countries. The study also identified a clustering of keywords around specific topics such as depression, anxiety, and prevalence. To further enhance research collaboration, we suggest the development of global thematic networks that prioritize underrepresented areas, such as adolescent mental health. Addressing these findings through improved methodologies and interdisciplinary approaches can enhance future research, ensuring that the findings can be translated into effective mental health interventions for students.

Author contributions

Conceptualization: Irum Zeb.

Formal analysis: Aashiq Khan, Yuhao Su.

Methodology: Irum Zeb.

Software: Aashiq Khan, Yuhao Su.

Visualization: Aashiq Khan, Irum Zeb, Yuhao Su.

Writing – original draft: Aashiq Khan.

Writing – review & editing: Irum Zeb, Yuhao Su.

Abbreviations:

AI
artificial intelligence
NIH
national institutes of health
NLP
natural language processing
PRISMA
preferred reporting items for systematic reviews and meta-analyses
STROBE
strengthening the reporting of observational studies in epidemiology
VOS
visualization of similarities
WOS
web of science

No consent was required for this study.

The authors have no funding and conflicts of interest.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Zeb I, Khan A, Su Y. Spectrum of mental health among students: A scientometric analysis of 20 years of research on depression and anxiety. Medicine 2025;104:49(e46751).

IZ and AK contributed to this article equally.

Contributor Information

Irum Zeb, Email: ashiqkhan.edu@gmail.com.

Aashiq Khan, Email: irumzeb.kiu@gmail.com.

References

  • [1].Blanco C, Okuda M, Wright C, et al. Mental health of college students and their non–college-attending peers: results from the national epidemiologic study on alcohol and related conditions. Arch Gen Psychiatry. 2008;65:1429–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Lipson SK, Lattie EG, Eisenberg D. Increased rates of mental health service utilization by US college students: 10-year population-level trends (2007–2017). Psychiatr Serv. 2019;70:60–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [3].Hammoudi Halat D, Soltani A, Dalli R, Alsarraj L, Malki A. Understanding and fostering mental health and well-being among university faculty: a narrative review. J Clin Med. 2023;12:4425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Wuthrich VM, Jagiello T, Azzi V. Academic stress in the final years of school: a systematic literature review. Child Psychiatry Hum Dev. 2020;51:986–1015. [DOI] [PubMed] [Google Scholar]
  • [5].You Y, Wei M, Chen Y, et al. The association between recreational physical activity and depression in the short sleep population: a cross-sectional study. Front Neurosci. 2023;17:1016619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Auerbach RP, Mortier P, Bruffaerts R, et al.; WHO WMH-ICS Collaborators. WHO world mental health surveys international college student project: prevalence and distribution of mental disorders. J Abnorm Psychol. 2018;127:623–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Statistics N. Nontraditional Undergraduates. USDoE Institute of Education Sciences, Editor; 2013.
  • [8].Arnett JJ. Emerging adulthood: a theory of development from the late teens through the twenties. Am Psychol. 2000;55:469–80. [PubMed] [Google Scholar]
  • [9].Eisenberg D, Golberstein E, Gollust SE. Help-seeking and access to mental health care in a university student population. Med Care. 2007;45:594–601. [DOI] [PubMed] [Google Scholar]
  • [10].Hyseni Duraku Z, Davis H, Arënliu A, Uka F, Behluli V. Overcoming mental health challenges in higher education: a narrative review. Front Psychol. 2024;15:1466060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Eisenberg D, Hunt J, Speer N, Zivin K. Mental health service utilization among college students in the United States. J Nerv Ment Dis. 2011;199:301–8. [DOI] [PubMed] [Google Scholar]
  • [12].You Y, Li J, Zhang Y, Li X, Li X, Ma X. Exploring the potential relationship between short sleep risks and cognitive function from the perspective of inflammatory biomarkers and cellular pathways: insights from population‐based and mice studies. CNS Neurosci Ther. 2024;30:e14783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].You Y, Ablitip A, Chen Y, et al. Saturation effects of the relationship between physical exercise and systemic immune inflammation index in the short-sleep population: a cross-sectional study. BMC Public Health. 2024;24:1920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Lattie EG, Adkins EC, Winquist N, Stiles-Shields C, Wafford QE, Graham AK. Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: systematic review. J Med Internet Res. 2019;21:e12869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Faisal RA, Jobe MC, Ahmed O, Sharker T. Mental health status, anxiety, and depression levels of Bangladeshi university students during the COVID-19 pandemic. Int J Ment Health Addict. 2022;20:1500–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Guney S, Kalafat T, Boysan M. Dimensions of mental health: life satisfaction, anxiety and depression: a preventive mental health study in Ankara University students population. Procedia Soc Behav Sci. 2010;2:1210–3. [Google Scholar]
  • [17].Dalky HF, Gharaibeh A. Depression, anxiety, and stress among college students in Jordan and their need for mental health services. Nurs Forum. 2019;54:205–12. [DOI] [PubMed] [Google Scholar]
  • [18].Sabe M, Chen C, Sentissi O, et al. Thirty years of research on physical activity, mental health, and wellbeing: a scientometric analysis of hotspots and trends. Front Public Health. 2022;10:943435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Klarin A. How to conduct a bibliometric content analysis: guidelines and contributions of content co‐occurrence or co‐word literature reviews. Int J Consum Stud. 2024;48:e13031. [Google Scholar]
  • [20].Paykel ES. Basic concepts of depression. Dialogues Clin Neurosci. 2008;10:279–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Vetrivel S, Saravanan T, Maheswari R, Arun V. Coping Mechanisms for Stress Among Students at Universities. In: Student Well-Being in Higher Education Institutions. IGI Global; 2024:157–86. [Google Scholar]
  • [22].Niu Y, Adam M, Hussein H. Connecting urban green spaces with children: a scientometric analysis using CiteSpace. Land. 2022;11:1259. [Google Scholar]
  • [23].You Y, Li W, Liu J, Li X, Fu Y, Ma X. Bibliometric review to explore emerging high-intensity interval training in health promotion: a new century picture. Front Public Health. 2021;9:697633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].You Y, Wang D, Wang Y, Li Z, Ma X. A bird’s-eye view of exercise intervention in treating depression among teenagers in the last 20 years: a bibliometric study and visualization analysis. Front Psychiatry. 2021;12:661108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Mejia C, Wu M, Zhang Y, Kajikawa Y. Exploring topics in bibliometric research through citation networks and semantic analysis. Front Res Metr Anal. 2021;6:742311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Fu JN, Yu WB, Li SQ, Sun WZ. A bibliometric analysis of anxiety and depression among primary school students. Front Psychiatry. 2024;15:1431215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Liu G-Y, Yan M-D, Mai Y-Y, et al. Frontiers and hotspots in anxiety disorders: a bibliometric analysis from 2004 to 2024. Heliyon. 2024;10:e35701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Xu Y, Zeng J, Chen W, Jin R, Li B, Pan Z. A holistic review of cement composites reinforced with graphene oxide. Constr Build Mater. 2018;171:291–302. [Google Scholar]
  • [29].Darko A, Chan AP, Huo X, Owusu-Manu D-G. A scientometric analysis and visualization of global green building research. Build Environ. 2019;149:501–11. [Google Scholar]
  • [30].Amin MN, Ahmad W, Khan K, Sayed MM. Mapping research knowledge on rice husk ash application in concrete: a scientometric review. Materials. 2022;15:3431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Markoulli MP, Lee CI, Byington E, Felps WA. Mapping human resource management: reviewing the field and charting future directions. Hum Resour Manag Rev. 2017;27:367–96. [Google Scholar]
  • [32].Afgan S, Bing C. Scientometric review of international research trends on thermal energy storage cement based composites via integration of phase change materials from 1993 to 2020. Constr Build Mater. 2021;278:122344. [Google Scholar]
  • [33].Huang S, Wang H, Ahmad W, et al. Plastic waste management strategies and their environmental aspects: a scientometric analysis and comprehensive review. Int J Environ Res Public Health. 2022;19:4556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Jin R, Gao S, Cheshmehzangi A, Aboagye-Nimo E. A holistic review of off-site construction literature published between 2008 and 2018. J Clean Prod. 2018;202:1202–19. [Google Scholar]
  • [35].Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84:523–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Chen Y, You Y, Wang Y, Wang Y, Dai T. Global insights into rural health Workers’ job satisfaction: a scientometric perspective. Front Public Health. 2022;10:895659. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Wuni IY, Shen GQ, Osei-Kyei R. Scientometric review of global research trends on green buildings in construction journals from 1992 to 2018. Energy Build. 2019;190:69–85. [Google Scholar]
  • [38].Weismayer C, Pezenka I. Identifying emerging research fields: a longitudinal latent semantic keyword analysis. Scientometrics. 2017;113:1757–85. [Google Scholar]
  • [39].Aksnes DW, Langfeldt L, Wouters P. Citations, citation indicators, and research quality: an overview of basic concepts and theories. Sage Open. 2019;9:2158244019829575. [Google Scholar]
  • [40].Kessler RC, Petukhova M, Sampson NA, Zaslavsky AM, Wittchen HU. Twelve‐month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int J Methods Psychiatr Res. 2012;21:169–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Fiske A, Wetherell JL, Gatz M. Depression in older adults. Annu Rev Clin Psychol. 2009;5:363–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Kessler RC, Aguilar-Gaxiola S, Alonso J, et al. The global burden of mental disorders: an update from the WHO World Mental Health (WMH) surveys. Epidemiol Psichiatr Soc. 2009;18:23–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Teachman BA, McKay D, Barch DM, Prinstein MJ, Hollon SD, Chambless DL. How psychosocial research can help the National Institute of Mental Health achieve its grand challenge to reduce the burden of mental illnesses and psychological disorders. Am Psychol. 2019;74:415–31. [DOI] [PubMed] [Google Scholar]
  • [44].Proctor EK, Landsverk J, Baumann AA, et al. The implementation research institute: training mental health implementation researchers in the United States. Implement Sci. 2013;8:1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [45].Jiang X, Borg E, Borg M. Challenges and coping strategies for international publication: perceptions of young scholars in China. Stud Higher Educ. 2017;42:428–44. [Google Scholar]
  • [46].Sharan P, Gallo C, Gureje O, et al.; for the World Health Organization–Global Forum for Health Research – Mental Health Research Mapping Project Group. Mental health research priorities in low-and middle-income countries of Africa, Asia, Latin America and the Caribbean. Br J Psychiatry. 2009;195:354–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Chen J, Liu Y, Zhu K, Dai J, Wang C. The development and evolution of the research topic on the mental health of college students: a bibliometric review based on CiteSpace and VOSviewer. Heliyon. 2024;10:e29477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [48].Hassan W, Duarte AE. Bibliometric analysis: a few suggestions. Curr Probl Cardiol. 2024;49:102640. [DOI] [PubMed] [Google Scholar]
  • [49].Mirbahaeddin E, Chreim S. A narrative review of factors influencing peer support role implementation in mental health systems: implications for research, policy and practice. Adm Policy Ment Health. 2022;49:596–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [50].Rodríguez DC, Jessani NS, Zunt J, et al. Experiential learning and mentorship in global health leadership programs: capturing lessons from across the globe. Ann Glob Health. 2021;87:61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [51].Lin N, Zhou X, Wang Y, et al. Effect of enhanced recovery after radiotherapy (ERAR) on the quality of life in patients with nasopharyngeal carcinoma after radiotherapy: a randomized controlled trial. Oral Oncol. 2025;164:107269. [DOI] [PubMed] [Google Scholar]
  • [52].Ju F. Mapping the knowledge structure of image recognition in cultural heritage: a scientometric analysis using CiteSpace, VOSviewer, and Bibliometrix. J Imaging. 2024;10:272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [53].Haghani M. What makes an informative and publication-worthy scientometric analysis of literature: a guide for authors, reviewers and editors. Transp Res Interdiscip Perspect. 2023;22:100956. [Google Scholar]
  • [54].Matsubara Y, Singh S, eds. Citations beyond self citations: identifying authors, affiliations, and nationalities in scientific papers. In: Proceedings of the 8th International Workshop on Mining Scientific Publications; 2020. [Google Scholar]
  • [55].Zupic I, Čater T. Bibliometric methods in management and organization. Organ Res Methods. 2015;18:429–72. [Google Scholar]
  • [56].Dergaa I, Ben Saad H. Artificial intelligence and promoting open access in academic publishing. Tunis Med. 2023;101:533–6. [PMC free article] [PubMed] [Google Scholar]
  • [57].Gao J, Choo KTW, Cao J, Lee RK-W, Perrault S. CoAIcoder: examining the effectiveness of AI-assisted human-to-human collaboration in qualitative analysis. ACM Trans Comput-Hum Interact. 2023;31:1–38. [Google Scholar]
  • [58].Pattnaik D, Ray S, Raman R. Applications of artificial intelligence and machine learning in the financial services industry: a bibliometric review. Heliyon. 2024;10:e23492. [DOI] [PMC free article] [PubMed] [Google Scholar]

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