“Without this, I would for sure already be dead”: A qualitative inquiry regarding suicide protective factors among trans adults.
Bibliographic record
Abstract
Despite an alarmingly high rate of attempted suicide among trans adults, few studies have investigated suicide protective factors among this population. The current study was aimed at identifying suicide protective factors among trans adults using a qualitative methodology. A sample of self-identified trans adults (N = 133) was recruited from LGBT LISTSERVs across Canada. Participant were predominantly White and ranged in age from 18 to 75 years old (M = 37). Qualitative data were collected online via open-ended questions and analyzed using thematic network analysis. A hybrid inductive-deductive coding framework was created by combining published suicide protective factors and participants' responses. Five organizing themes were identified, namely social support, gender identity-related factors, transition-related factors, individual difference factors, and reasons for living. RESULTS provide important insights for suicide prevention workers and mental/medical health professionals who work to promote the health and well-being of trans clients and their families. Clinical implications are discussed, such as the importance of aiding trans clients who seek transition-related care to gain access to care in a timely manner. Language: en
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".