Suicidality Among Transgender Youth: Elucidating the Role of Interpersonal Risk Factors
Bibliographic record
Abstract
Data indicate that 82% of transgender individuals have considered killing themselves and 40% have attempted suicide, with suicidality highest among transgender youth. Using minority stress theory and the interpersonal theory of suicide, this study aims to better understand suicide risk among transgender youth. The present study examines the influence of intervenable risk factors: interpersonal and environmental microaggressions, internalized self-stigma, and adverse childhood experiences (ACEs), and protective factors: school belonging, family support, and peer support on both lifetime suicide attempts and past 6-month suicidality in a sample of transgender youth ( n = 372). SPSS 22 was utilized to examine the impact of the independent variables on both suicidality and lifetime suicide attempt through two separate logistic regressions. Fifty six percent of youth reported a previous suicide attempt and 86% reported suicidality. Logistic regressions indicated that models for both lifetime suicide attempts and suicidality were significant. Interpersonal microaggressions, made a unique, statistically significant contribution to lifetime suicide attempts and emotional neglect by family approached significance. School belonging, emotional neglect by family, and internalized self-stigma made a unique, statistically significant contribution to past 6-month suicidality. Results have significant practice and policy implications. Findings offer guidance for practitioners working with parents and caregivers of trans youth, as well as, for the creation of practices which foster interpersonal belonging for transgender youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".