HIV-negative gay men's accounts of using context-dependent sero-adaptive strategies
Bibliographic record
Abstract
We enrolled 166 gay and bisexual men who tested HIV-negative at a community sexual health clinic in Vancouver, British Columbia, into a year-long mixed-methods study. A subsample of participants who reported recent condomless anal sex (n = 33) were purposively recruited into an embedded qualitative study and completed two in-depth qualitative interviews. Analysis of baseline interviews elicited three narratives relevant to men's use of context- or relationally-dependent HIV-risk management strategies: (1) seroadaptive behaviours such as partner testing and negotiated safety agreements used with primary sexual partners, (2) serosorting and seroguessing when having sex with new partners and first-time hookups and (3) seroadaptive behaviours, including one or more of seropositioning/strategic positioning, condom serosorting and viral load sorting, used by participants who knowingly had sex with a serodiscordant partner. Within men's talk about sex, we found complex and frequently biomedically-informed rationale for seroadaptation in men's decisions to have what they understood to be various forms of safe or protected condomless anal sex. Our findings support the need for gay men's research and health promotion to meaningfully account for the multiple rationalities and seroadaptive strategies used for having condomless sex in order to be relevant to gay men's everyday sexual decision-making.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".