Depression and Suicide Literacy among Canadian Sexual and Gender Minorities
Bibliographic record
Abstract
The purpose of this study was to examine and compare depression and suicide literacy among Canadian sexual and gender minorities (SGM). Online surveys comprised of the 22-item depression literacy scale (D-LIT) and the 12-item literacy of suicide scale (LOSS) were completed by 2,778 individuals identifying as SGM. Relationships between depression and suicide literacy and demographic characteristics were evaluated using multivariable linear regression. Overall, SGM correctly answered 71.3% of the questions from the D-LIT and 76.5% of the LOSS. D-LIT scores were significantly lower among cisgender men and D-LIT and LOSS scores were lower among transgender women when compared to cisgender women. LOSS and D-LIT scores were significantly lower among SGM without a university degree (compared to those with a university degree) and among SGM from ethnic minority groups (compared to White SGM). D-LIT scores, but not LOSS scores, were significantly lower among Indigenous SGM compared to White SGM. The findings provide evidence of differences in suicide and depression literacy between SGM subgroups along multiple social axes. Interventions to increase depression and suicide literacy should be prioritized as part of a mental health promotion strategy for SGM, targeting subgroups with lower literacy levels, including cisgender men, transgender women, Indigenous people, racialized minorities, and those without a university degree.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".