A mixed method analysis of differential reasons for condom use and non-use among gay, bisexual, and other men who have sex with men
Bibliographic record
Abstract
We sought to examine how condom use was differentially reasoned by gay, bisexual and other men who have sex with other men (GBM) in Ontario, Canada. Data were derived from a community-based study of GBM who completed an anonymous online questionnaire in 2014. Participants qualitatively described reasons a condom was used or not at their most recent anal sex event. Qualitative responses were thematically coded non-exclusively and associations with event-level and individual-level factors were determined quantitatively using manual backward stepwise multivariable logistic regression. Among 1,830 participants, 1,460 (79.8%) reported a recent anal sex event, during which 884 (60.6%) used condoms. Reasons for condom use included protection/safety (82.4%), norms (30.5%), and combination prevention (6.2%). Reasons for non-use were intentional (43.1%), trust (27.6%), unintentional (25.7%), and other strategies (19.6%). Event-level substance use was associated with all non-use reasons: e.g., more likely to be unintentional, less likely to be trust. Condom non-use with online-met partners was associated with more intentional and unintentional reasons and less trust reasons. Non-white and bisexual GBM were less likely to explain condom use as a norm. Participant-partner HIV status was an important predictor across most condom use and non-use reasons: e.g., sero-different partnerships were more likely to reason condom use as combination prevention and condom non-use as trust, unknown status partnerships were more likely to reason non-use as unintentional. Condom use among GBM is a multi-faceted practice, especially with increasing antiretroviral-based HIV prevention. Future interventions must adapt to changing GBM (sub-)cultures with targeted, differentiated, culturally-appropriate, and sustained interventions.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".