Prevalence and Co-Occurrence of Heavy Drinking and Anxiety and Mood Disorders Among Gay, Lesbian, Bisexual, and Heterosexual Canadians
Bibliographic record
Abstract
OBJECTIVES: To investigate the prevalence and co-occurrence of heavy drinking, anxiety, and mood disorders among Canadians who self-identified as gay, lesbian, bisexual, or heterosexual. METHODS: Pooled data from the 2007 to 2012 cycles of the Canadian Community Health Survey (n = 222 548) were used to fit logistic regression models controlling for sociodemographic characteristics. RESULTS: In adjusted logistic regression models, gay or lesbian respondents had greater odds than heterosexual respondents of reporting anxiety disorders, mood disorders, and anxiety-mood disorders. Bisexual respondents had greater odds of reporting anxiety disorders, mood disorders, anxiety-mood disorders, and heavy drinking. Gay or lesbian and bisexual respondents had greater odds than heterosexuals of reporting co-occurring anxiety or mood disorders and heavy drinking. The highest rates of disorders were observed among bisexual respondents, with nearly quadruple the rates of anxiety, mood, and combined anxiety and mood disorders relative to heterosexuals and approximately twice the rates of gay or lesbian respondents. CONCLUSIONS: Members of sexual minority groups in Canada, in particular those self-identifying as bisexual, experience disproportionate rates of anxiety and mood disorders, heavy drinking, and co-occurring disorders.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".