Prevalence of suicidal thoughts and attempts in the transgender population of the world: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The aim of this meta-analysis was to determine global pooled prevalence of suicide thoughts and attempts in transgender population. Methods For doing comprehensive search strategy related to objectives in the presence meta-analysis, all international databases like PubMed (Medline), Scopus, Embase, Web of Sciences, PsycINFO, and the Cumulative Index to Nursing and Allied Health Literature (CINHAL) were searched from January 1990 to December 2022. The quality of the final selected studies was evaluated according to Newcastle–Ottawa Quality Assessment Scale for cross-sectional studies. The subgroup analysis was done based on type of transgender (female to male, male to female) and prevalence (point, period, and lifetime), country, and criteria of diagnosis. All analysis was done in STATA version 17. Results From the total number of 65 selected studies, 71 prevalence of suicidal thoughts, including point, period, and lifetime prevalence were extracted and combined. After combining these values, the prevalence of suicidal thoughts in the transgender population in the world was 39% in the past month (pooled point prevalence: 39%; 95% CI 35–43%), 45% in the past year (pooled period prevalence: 45%; % 95 CI 35–54%) and 50% during lifetime (pooled lifetime prevalence: 50%; % 95 CI 42–57%). Also, the prevalence of suicide attempt in the transgender population of the world was 16% in the past month (pooled point prevalence: 16%; 95% CI 13–19%), 11% in the past year (pooled period prevalence: 11%; % 95 CI 5–19%) and 29% during lifetime (pooled lifetime prevalence: 29%; % 95 CI 25–34%). Conclusion The present meta-analysis results showed the prevalence of suicidal thoughts and attempts in the transgender community was high, and more importantly, about 50% of transgenders who had suicidal thoughts, committed suicide.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".