A tale of two epidemics: gay men's mental health and the biomedicalisation of <scp>HIV</scp> prevention and care in Toronto
Bibliographic record
Abstract
There is mounting urgency regarding the mental health of gay, bisexual and other men who have sex with men (GBM). We examined how GBM are understanding the relationship between HIV and their mental health given the increasing biomedicalisation of HIV prevention and care. Our Grounded Theory analysis derived from qualitative interviews with 24 GBM living in Toronto, Canada, including both HIV-negative and HIV-positive men. Participants understood biomedical advances, such as undetectable viral load and pre-exposure prophylaxis (PrEP), as providing some relief from HIV-related distress. However, they offered ambivalent perspectives on the biomedicalisation of HIV. Some considered non-HIV-specific stressors (e.g. unemployment, racial discrimination) more significant than HIV-related concerns. These men expressed HIV-related distress as being under control due to biomedical advances or as always negligible when compared to non-HIV-specific stressors. Others emphasised the ongoing mental health implications of HIV (e.g. enduring risk and stigma). We describe a tension between optimistic responses to biomedicine's ability to ease the psychosocial burdens associated with HIV and the inability for biomedicine to address the social and economic determinants driving the dual epidemics of HIV and mental distress amongst GBM. We argue for more socio-material analysis over further sexual behavioural analysis of GBM mental health disparities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".