Mental Health and Substance Use among Bisexual Youth and Non-Youth in Ontario, Canada
Bibliographic record
Abstract
Research has shown that bisexuals have poorer health outcomes than heterosexuals, gays, or lesbians, particularly with regard to mental health and substance use. However, research on bisexuals is often hampered by issues in defining bisexuality, small sample sizes, and by the failure to address age differences between bisexuals and other groups or age gradients in mental health. The Risk & Resilience Survey of Bisexual Mental Health collected data on 405 bisexuals from Ontario, Canada, using respondent-driven sampling, a network-based sampling method for hidden populations. The weighted prevalence of severe depression (PHQ-9 ≥ 20) was 4.7%, possible anxiety disorder (OASIS ≥ 8) was 30.9%, possible post-traumatic stress disorder (PCL-C ≥ 50) was 10.8%, and past year suicide attempt was 1.9%. With respect to substance use, the weighted prevalence of problem drinking (AUDIT ≥ 5) was 31.2%, and the weighted prevalence of illicit polydrug use was 30.5%. Daily smoking was low in this sample, with a weighted prevalence of 7.9%. Youth (aged 16-24) reported significantly higher weighted mean scores on depression and post-traumatic stress disorder, and higher rates of past year suicidal ideation (29.7% vs. 15.2%) compared with those aged 25 and older. The burden of mental health and substance use among bisexuals in Ontario is high relative to population-based studies of other sexual orientation groups. Bisexual youth appear to be at risk for poor mental health. Additional research is needed to understand if and how minority stress explains this burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 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) | 0.003 | 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".