Exploring Cross-Sectional Predictors of Suicide Ideation, Attempt, and Risk in a Large Online Sample of Transgender and Gender Nonconforming Youth and Young Adults
Bibliographic record
Abstract
PURPOSE: Rates of suicide ideation and attempt appear to be particularly high in the transgender and gender nonconforming (TGNC) population, yet little is known about which factors are the most salient contributors for TGNC young people and how these contributors vary across suicide-related outcomes. METHODS: Within the largest sample of TGNC young people to date (N = 1896; ages 14-30), we examined the contribution of demographics (age, assigned sex, gender identity, sexual orientation identity, race/ethnicity, and socioeconomic status), minority stress (gender-related affirmation, gender-related self-concept, victimization, and gender-affirming medical treatment desire/access), social support (from family and friends), and depressive symptoms in the cross-sectional prediction of three suicide-related outcomes: past-year attempt, past-year ideation, and a composite measure of suicide risk. RESULTS: Each set of factors explained significant variance in each outcome; however, only several predictors remained significant in each of the full models. Gender-related victimization and depressive symptoms were independent predictors for all three outcomes. Additional predictors varied across outcome. Age, male identity, sexual orientation-based victimization, and friend support were associated with suicide attempt. Age, queer identity, gender-related self-concept negativity, and family support were associated with suicide ideation, and pansexual identity and gender-related self-concept negativity were associated with positive suicide risk screen. CONCLUSION: Prevention and intervention efforts aimed at building support and positive self-concept, decreasing victimization, and treating depression are likely to partially reduce suicide ideation and attempt in TGNC adolescents and young adults. Comprehensive interventions with younger adolescents are particularly critical.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".