Research Publications on the Mental Health of Transgender People: A Bibliometric Analysis Using Scopus Database (1992–2021)
Bibliographic record
Abstract
Purpose: Assessing research activity is important for planning future research agendas and corresponding policies. The purpose of the current study was to analyze research publications on the mental health of transgender people. Methods: A bibliometric method using the SciVerse Scopus database was conducted. The study period was from 1992 to 2021. Keywords related to transgender and mental health were used to generate bibliometric data. Results: The search strategy found 1862 documents authored by 7820 researchers and disseminated through 641 journals. Research on the mental health of transgender people experienced a steep growth after 2013. Authors and institutions in the United States were the most active in the field. Except for research collaboration between the United States and Canada, no significant cross-country collaboration was noted in the field. The most active journal was the Journal of Gay and Lesbian Mental Health ( n =54, 2.9%), followed by the LGBT Health journal ( n =52, 2.8%). However, documents published in the American Journal of Public Health journal ( n =147.9) received the highest number of citations per document. Articles on suicide, violence, mental stress, and stigma were the most impactful in terms of the number of citations. Major research themes in the field included substance/alcohol use, violence, and the prevalence of depression/anxiety/suicide among transgender youth. Conclusions: Research on the mental health of transgender people is on the rise. International research collaboration in the field is important to help researchers in low- and middle-income countries and increase the visibility of problems about transgender people in these countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.243 | 0.253 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".