Widening mental health and substance use inequities among sexual and gender minority populations: Findings from a repeated cross-sectional monitoring survey during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
This paper examines the mental health and substance use impacts of the COVID-19 pandemic among sexual and gender minority (SGM) populations as compared to non-SGM populations, and identifies risk factors for mental health and substance use impacts among SGM groups. Data were drawn from two rounds of a repeated cross-sectional monitoring survey of 6027 Canadian adults, with Round 1 conducted May 14-19, 2020 and Round 2 conducted September 14-21, 2020. Bivariate cross-tabulations with chi-square tests were utilized to identify differences in mental health and substance use outcomes between SGM and non-SGM groups. Separate multivariable logistic regression models were used to identify risk factors for mental health and substance use outcomes for all SGM respondents. Compared to non-SGM respondents, a greater proportion of SGM participants reported mental health and substance use impacts of the COVID-19 pandemic, including deterioration in mental health, poor coping, suicidal thoughts, self-harm, alcohol and cannabis use, and use of substances to cope. Among SGM respondents, various risk factors, including having a pre-existing mental health condition, were identified as associated with mental health and substance use impacts. These widening inequities demonstrate the need for tailored public mental health actions during and beyond the pandemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".