Bibliographic record
Abstract
A recent study indicated a suicide attempt rate of 41 % among trans (e.g., trans, transgender, transexual/transsexual, genderqueer, two-spirit) individuals. Although this rate is alarming, there is a dearth of literature regarding suicide prevention for trans individuals. A vital step in developing suicide prevention models is the identification of protective factors. It was hypothesized that social support from friends, social support from family, optimism, reasons for living, and suicide resilience, which are known to protect cis (non-trans) individuals, also protect trans individuals. A sample of self-identified trans Canadian adults (N = 133) was recruited from LGBT and trans LISTSERVs. Data were collected online using a secure survey platform. A three block hierarchical multiple regression model was used to predict suicidal behavior from protective factors. Social support from friends, social support from family, and optimism significantly and negatively predicted 33 % of variance in participants' suicidal behavior after controlling for age. Reasons for living and suicide resilience accounted for an additional 19 % of the variance in participants' suicidal behavior after controlling for age, social support from friends, social support from family, and optimism. Of the factors mentioned above, perceived social support from family, one of three suicide resilience factors (emotional stability), and one of six reasons for living (child-related concerns) significantly and negatively predicted participants' suicidal behavior. Overall, these findings can be used to inform the practices of mental health workers, medical doctors, and suicide prevention workers working with trans clients.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".