Determinants of transgender individuals’ well-being, mental health, and suicidality in a rural state.
Bibliographic record
Abstract
This project utilized a Community-Based Participatory Research (CBPR) approach to conduct qualitative interviews with 30 transgender adults living in a rural state. Participants' identities spanned from trans women and men to non-binary and Two-Spirit. The aim of this study was to better understand the experiences, needs, and priorities of the participants as well as to examine possible determinants of mental health, well-being, and suicidality for transgender individuals in Montana. These factors were investigated at individual, interpersonal, community, and societal levels using an ecological framework. Qualitative results indicate that participants experienced discrimination at all levels. Participants noted that discrimination contributed to mental health challenges and limited access to adequate general and transgender-specific healthcare services, both of which impacted overall well-being. This is reflected most notably in the elevated rate of past suicidal ideation attempts among the sample. Participants reported that the ability to transition, as well as other protective factors, played a role in reducing suicidality and improving mental and physical health. Our findings highlight the need to address transgender mental health through implementing changes at multiple ecological levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".