The rural side of the rainbow: Mental health and the intersections of geography, sexuality, and partnership
Bibliographic record
Abstract
Lesbian, gay, and bisexual (LGB) persons tend to be geographically concentrated in larger metropolitan areas and research persistently observes LGB persons as a disadvantaged population for mental health outcomes when compared to their heterosexual counterparts. Conflicting evidence suggests that mental health risk exposures are greater for LGB people in rural spaces while other research posits that urban residency is more detrimental for LGB mental health. One positively contributing factor to the mental well-being of LGB persons is their partnership status. To date, no study estimates how partnership may ameliorate unfavourable mental health outcomes for LGB populations in urban and rural areas. Using 10 years of pooled data from the nationally representative Canadian Community Health Survey (CCHS), this study examines mental health and the intersection of sexuality, geographic residency, and partnership. Logistic regression models estimate the intersections of sexuality, geography, and partnership status on mental health, stratified by respondents' gender. Findings show partnered gay men in rural areas experiencing better mental health than their partnered heterosexual counterparts in the largest urban cities. Although not significant, the same pattern is observed for partnered lesbian women who do not experience a significant mental health disadvantage at any geographic level. Regardless of partnership and geographic space, bisexual men, and especially bisexual women, exhibit worse mental health outcomes compared to their heterosexual counterparts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".