Transition Milestones, Psychological Distress, and Suicidal Ideation Among Transgender Adults: A Structural Equation Analysis
Bibliographic record
Abstract
PURPOSE: This paper examines the relationships among transition milestones (i.e., transition-related events in transgender persons' lives that demarcate their life circumstances before vs. afte a milestone was reached), psychological distress, and suicidal ideation in a large sample of transgender adults. METHODS: Data from the 2015 U.S. National Transgender Survey were used to examine 11 specific transition milestones in a sample of 27,715 transgender Americans aged 18 or older. The Kessler-6 scale was used to measure psychological distress and a dichotomous measure of suicidal ideation during the past year was the other main outcome measure. Covariates in the multivariate analysis included demographic measures, variables assessing support and discrimination, and 11 transition milestones. RESULTS: Bivariate analyses revealed that, in almost all instances, reaching specific transition milestones led to reduced psychological distress and diminished odds of suicidal ideation. Multivariate analysis revealed that psychological distress was a strong predictor of suicidal ideation, but transition milestones were not retained in the final model. Structural equation analysis showed that three specific transition milestones (namely, changing one's name and/or gender on legal documents, taking gender-affirming hormones, having had any gender-conforming surgical procedures) influenced suicidal ideation indirectly, through their direct impact on psychological distress. CONCLUSIONS: Reaching specific transition milestones plays an important role in many transgender adults' lives, and may be highly beneficial in helping them to reduce psychological distress. This, in turn, is likely to have a positive impact upon their likelihood of contemplating 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".