Between two pandemics: Older, gay men's experiences across HIV/AIDS and COVID-19
Bibliographic record
Abstract
Pandemics are a component of human life, and have had great bearing on the trajectory of human evolution. Historically, the biomedical aspects of pandemics have been overrepresented, but there is growing recognition of the degree to which pandemics are socially and culturally embedded, highlighting how virus perception is socially and politically informed. Older (50+), gay men represent a population who have experienced two global pandemics in their lifespans: HIV/AIDS and COVID-19. Although governments and health officials largely failed gay men during the HIV/AIDS pandemic, gay men represent an important source of pandemic information and their experiences have much to offer health professionals and policymakers. As such, a small but growing body of literature has compared gay men's experiences amidst the two pandemics. The current study drew on constructivist grounded theory methods to examine how living through the HIV/AIDS pandemic has influenced older gay men's perspectives of COVID-19. Twenty Canadian-based gay men aged 50+ participated in semi-structured interviews via Zoom. Analysis revealed three key processes: (1) uncertainty and the familiarity of loss, (2) witnessing pandemic inequities, and, (3) navigating constantly evolving (mis)information. We highlight the utility of this knowledge to informing future pandemic planning and policies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".