Disparities in positive mental health of sexual and gender minority adults in Canada
Bibliographic record
Abstract
INTRODUCTION: The goal of this study was to examine potential disparities in positive mental health (PMH) among adults in Canada by sexual orientation and gender modality. METHODS: Using 2019 Canadian Community Health Survey (CCHS) Annual Component data (N = 57 034), we compared mean life satisfaction and the prevalence of high self-rated mental health (SRMH), happiness and community belonging between heterosexual and sexual minority adults, and between cisgender and gender minority adults. We used 2019 CCHS Rapid Response on PMH data (N = 11 486) to compare the prevalence of high psychological well-being between heterosexual and sexual minority adults. Linear and logistic regression analyses examined the between-group differences in mean life satisfaction and the other PMH outcomes, respectively. RESULTS: Sexual minority (vs. heterosexual) adults reported lower mean life satisfaction (B = -0.7, 95% CI: -0.8, -0.5) and were less likely to report high SRMH (OR = 0.4, 95% CI: 0.3, 0.5), happiness (OR = 0.4, 95% CI: 0.3, 0.5), community belonging (OR = 0.6, 95% CI: 0.5, 0.7) and psychological well-being (OR = 0.4, 95% CI: 0.3, 0.6). Differences were not always significant for specific sexual minority groups in sexstratified analyses. Gender minority adults reported lower mean life satisfaction and were less likely to report high SRMH and happiness than cisgender adults. CONCLUSION: Future research could investigate how these PMH disparities arise, risk and protective factors in these populations, how other sociodemographic factors interact with sexual orientation and gender identity to influence PMH and changes in disparities over time.
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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 | 0.002 | 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) | 0.003 | 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".