Forging Resilience to HIV/AIDS: Personal Strengths of Middle-aged and Older Gay, Bisexual, and Other Men Who Have Sex With Men Living With HIV/AIDS
Bibliographic record
Abstract
HIV-positive gay, bisexual, two-spirit, and other men who have sex with men (MSM) have exhibited significant resilience to HIV/AIDS in Canada since the start of the epidemic. Since 2012, most of the research that has been conducted on resilience to HIV/AIDS has utilized quantitative methods and deficits-based approaches, with a preferential focus on the plight of young MSM. In order to address apparent gaps in research on HIV/AIDS resilience, we conducted a community-based participatory research qualitative study that utilized a strengths-based approach to examine the perspectives and lived experiences of HIV-positive, middle-aged and older MSM on their individual attributes that helped forge their HIV/AIDS resilience. We conducted 41 semistructured interviews with diverse, HIV-positive, middle-aged and older MSM from Central and Southwestern Ontario, Canada. From our thematic analysis of our interviews, we identified four themes, which represented personal strengths that fostered resilience to HIV/AIDS: (a) proactiveness, (b) perseverance, (c) having the right mindset, and (d) self-awareness with self-control. This article discusses the importance of these personal strengths to fostering HIV/AIDS resilience, and how community-based resources could potentially lessen the need to muster such personal strengths, or alternatively, cultivate them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".