Examining Associations Between Resilience and PrEP Use Among HIV negative GBM in Toronto, Montreal and Vancouver
Bibliographic record
Abstract
This study evaluated the association between resilience and PrEP use among a population-based sample of Canadian gay, bisexual, and other men who have sex with men (GBM). Sexually active GBM aged ≥ 16 years old were recruited via respondent-driven sampling (RDS) in Toronto, Montreal, and Vancouver from 02/2017 to 07/2019. We conducted a pooled cross-sectional analysis of HIV-negative/unknown GBM who met clinical eligibility for PrEP. We performed multivariable RDS-II-weighted logistic regression to assess the association between scores on the Connor-Davidson Resilience-2 Scale and PrEP. Mediation analyses with weighted logistic and linear regression were used to assess whether the relationship between minority stressors and PrEP use was mediated by resilience. Of 1167 PrEP-eligible GBM, 317 (27%) indicated they took PrEP in the past six months. Our multivariable model found higher resilience scores were associated with greater odds of PrEP use in the past six months (aOR = 1.13, 95%CI = 1.00, 1.28). We found that resilience reduced the effect of the association between heterosexist discrimination and PrEP use. Resilience also mediated the relationship between internalized homonegativity and PrEP use and mediated the effect of the association between LGBI acceptance concern and PrEP use. Overall, PrEP-eligible GBM with higher resilience scores had a greater odds of PrEP use in the past six months. We also found mixed results for the mediating role of resilience between minority stress and PrEP use. These findings underline the continued importance of strength-based factors in HIV prevention.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".