Ethno-racial variations in mental health symptoms among sexually-active gay, bisexual, and other men who have sex with men in Vancouver, Canada: a longitudinal analysis
Bibliographic record
Abstract
BACKGROUND: Minority stress from racism and heterosexism may uniquely interact to impact the mental health of racialized sexual minorities. We examined variations in anxiety and depressive symptoms by reported by ethno-racial identity among gay, bisexual, and other men who have sex with men (gbMSM) in Vancouver, Canada. METHODS: We recruited gbMSM aged ≥ 16 years from February 2012 to February 2015 using respondent-driven sampling (RDS). Participants completed computer assisted self-interviews (CASI) at enrollment and every 6 months until February 2017. We examined factors associated with moderate/severe anxiety and depression scores (> 10) on the Hospital Anxiety and Depression Scale (HADS) and differences in key explanatory variables including sociodemographic, psychosocial, and substance use factors. We used multivariable mixed effects models to assess whether moderate/severe scores were associated with ethno-racial identity across all visits. RESULTS: After RDS-adjustment, of 774 participants, 79.9% of participants identified as gay. 68.6% identified as white, 9.2% as Asian, 9.8% as Indigenous, 7.3% as Latin American, and 5.1% as other ethno-racial identities. Participants contributed a median of 6 follow-up visits (Q1-Q3: 4-7). In the multivariable analysis, Asian participants had decreased odds of moderate/severe anxiety scores compared to white participants (aOR = 0.39; 95% CI: 0.18-0.86), and Latin American participants had decreased odds of moderate/severe depression scores compared to both white (aOR = 0.17; 95% CI: 0.08-0.36) and Asian (aOR = 0.07; 95% CI: 0.02-0.20) participants. CONCLUSION: Asian and Latino gbMSM reported decreased mental health symptoms compared to white participants. Asian and Latino gbMSM in Vancouver appear to manage multiple minority stressors without adversely affecting their mental health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".