Suicide Protective Factors Among Trans Adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it