A Bibliometric Analysis on Mental Health Research Over the Past Two Decades
Bibliographic record
Abstract
The research examined mental health publications through a comprehensive bibliometric analysis from 2000 to 2024, encompassing 483 documents published in 279 outlets that had an annual growth rate of 9.5%. The research explored publication trends; leading journals; key authors; key affiliations; collaboration networks of authors, institutions and countries; co-occurrence networks and identified major trending topics and themes. Research has shown that mental health article output experienced a significant growth after 2018 because of new policies and worldwide mental health concerns. Three prominent journals, Psychiatric Services, BMC Psychiatry and The Lancet lead the field because they demonstrate the critical nature of psychiatric and public health research. This analysis highlighted the leading researchers as well as prominent scholarly works while demonstrating that Harvard University and the University of Toronto made significant research contributions. The co-occurrence analysis and thematic map revealed four principal subjects: mental health services, depression, public health policy research and socioeconomic influences. The research investigated international research collaborations where the USA had a leading position in global collaboration. The effect of COVID-19 on mental health and cognitive behavioural therapy were the emerging topics in this field of research. The findings provide essential information about ongoing research trends as well as about influential publications and collaborations that direct future studies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.028 | 0.208 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".