Mental Health and Social Support of Sexual and Gender Diverse People from Québec, Canada During the COVID-19 Crisis
Bibliographic record
Abstract
Purpose: Sexual and gender minority (SGM) people are at increased risk for psychological distress compared with cisgender heterosexual people. Specific SGM subgroups include lesbian, gay, bisexual, gender diverse, and asexual people who each experience unique psychosocial challenges that can result in different mental health outcomes. The coronavirus disease 2019 (COVID-19) pandemic may have further exacerbated mental health disparities among these groups. The aim of this study was to compare lesbian, gay, bisexual, gender diverse, asexual, and cisgender heterosexual people's mental health and social support during the first 4 months of the COVID-19 crisis. Methods: This study used a cross-sectional online survey from March 26th, 2020 to July 7th, 2020 in Québec, Canada. A total of 2908 individuals (n = 304 SGM people, n = 2604 cisgender heterosexual people) completed questionnaires measuring perceived social support, perceived stress, symptoms of depression and anxiety, as well as loneliness. Results: SGM people presented worse health outcomes than cisgender heterosexual people on all questionnaires (p < 0.001). Post hoc analyses showed that particularly marginalized SGM subgroups, including bisexual and asexual people, reported the poorest mental health. Moderation analyses revealed that the buffering effect of social support on depressive symptoms was four times stronger among SGM people (ΔR2 = 0.041; p < 0.001) than among cisgender heterosexual people (ΔR2 = 0.010; p < 0.001). Conclusion: This study suggests that fostering social connectedness among SGM people may be especially beneficial in buffering against distress in the face of a crisis.
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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 | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".