Prevalence rates and identification of nonconsensual sexual experiences among gay, bisexual, and other men who have sex with men in Canada
Bibliographic record
Abstract
Gay, bisexual, and other men who have sex with men (GBMSM) have been identified as a population at higher risk of experiencing non-consensual sexual experiences (NSEs). However, previous research studies examining the prevalence of NSEs in this population have been limited by inconsistent terminology and a lack of research on the topic. The main focus of this study was to compare the effectiveness of using self-labels versus behavioural indicators to measure the prevalence of NSEs experienced in adulthood in GBMSM and contribute to more accurate prevalence rates in the Canadian context. A total of 346 participants were recruited from various social media platforms in Canada. The study used a two-part question that asked participants if they had ever been sexually assaulted or raped, followed by a question about other nonconsensual sexual experiences. The study also used a formal behavioural measure, the Sexual Experiences Survey, to assess the prevalence of NSEs. The results indicated that a two-part question and the formal measure reported similar prevalence rates of NSEs—64.5% and 66.8% respectively. Overall prevalence of NSEs was very high in this sample, and people of colour and trans men reported even higher rates. Depending on the context, the parsimonious choice of questioning persons on the NSE history may be valid, however, only if applied in the context of asking for rape and assault as well as other NSEs that may not be captured by these definitions. The study highlights the importance of using consistent terminology and effective measurement methods when studying the prevalence of NSEs in GBMSM. These findings may have important implications for developing interventions and for obtaining more accurate prevalence rates in a variety of settings without having to use a longer, more formalized measure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".