Examining the relation between negative health care experiences and suicide ideation in transgender and nonbinary adults.
Bibliographic record
Abstract
Suicidal thoughts and behaviours (STBs) are more common among transgender and non-binary (TGNB) people relative to the general population. Nonetheless, research on correlates and predictors of STBs among TGNB people is lacking. This study used a mixed-methods design to examine whether negative healthcare experiences (NHEs) concurrently and prospectively predicted suicide ideation (SI) among 182 TGNB adults aged 18-55 (M=25.64, SD=6.63). Participants could complete the study online worldwide, although 84% resided in North America. At baseline, participants completed questionnaire measures of NHEs, SI, depression symptoms, social support and TGNB community connectedness. The NHE questionnaire included open textboxes and responses were analyzed in a reflexive thematic analysis. Participants were recontacted four months later, and STBs experienced since baseline were recorded. Participants frequently experienced NHEs; 169 (93%) reported at least one. Further, experiencing more NHEs was significantly associated with more frequent SI concurrently (𝜌=.61, p<.01) and prospectively (𝜌=.56, p<.01), but not when depression was included as a covariate. Community connectedness was associated with SI at baseline, even when controlling for social support and NHEs (p=.007), but not when controlling for depression. Contrary hypotheses, social support and community connectedness generally did not moderate the relationship between NHEs and SI. In qualitative analyses, we identified five themes that added context regarding participants’ adverse experiences in healthcare settings. Our results highlight contributors to suicide risk among TGNB people. Systematically improving healthcare experiences (access; care received) for these groups may ultimately contribute to suicide prevention efforts.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".