Suicidality and Nonsuicidal Self-Injury in Transgender and Gender Diverse Youth
Bibliographic record
Abstract
Importance: Transgender and gender diverse (TGD) youth face adversities that increase risks for suicidality and nonsuicidal self-injury (NSSI). However, substantial variability in prevalence rates have been reported across the literature. Objective: To provide meta-analytic syntheses of research investigating the prevalence of suicidal ideation, suicide attempts, and NSSI in TGD youth and to examine the moderating effects of sample characteristics and study methods on estimates. Design, Setting, and Participants: MEDLINE, Embase, CINAHL, Web of Science, Social Work Abstracts, Scopus, PsycINFO, Cochrane Central Register of Controlled Trials, and ProQuest Dissertations & Theses Global databases were searched in January 2025. This meta-analysis included studies of TGD youth aged 25 years and younger measuring suicidal ideation, suicide attempts, and/or NSSI published in English. These data were analyzed from June 2025 to September 2025. Data Extraction and Synthesis: Data extraction followed the PRISMA guidelines. Random-effects meta-analyses were used to derive the pooled prevalence estimates. Main Outcomes and Measures: Outcomes included prevalence rates of suicidal ideation, suicide attempts, and NSSI. Sample characteristics (ie, race and ethnicity and gender identity group) and study methods (ie, assessment modality, timeframe, geographical region, and study quality) acted as moderator variables. Results: From 137 studies, 262 nonoverlapping samples with a total of 131 429 TGD youth (mean age, 17.0 [range, 7.5-23.4] years) were included. Pooled prevalence estimates of suicidal ideation, suicide attempts, and NSSI were 48.8% (95% CI, 45.6%-52.0%), 26.2% (95% CI, 23.9%-28.6%), and 46.6% (95% CI, 40.5%-52.7%), respectively. Survey studies yielded higher prevalence rates across all outcomes compared with interview and medical record reviews. Significant moderating effects for ethnicity, gender identity group, timeframe, geographical region, and study quality varied across outcomes. Conclusions and Relevance: In this meta-analyis, pooled estimates suggested that 1 in 2 TGD youth globally are experiencing suicidal ideation and/or NSSI, and 1 in 4 have experienced a suicide attempt. These pooled estimates are 2 to 3.5 times greater in TGD youth compared with those established for cisgender peers. There is a critical need for improved prevention and intervention efforts to address TGD mental health concerns.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".