Nonconsensual Sexual Experience Histories of Incarcerated Men: A Mixed Methods Approach
Bibliographic record
Abstract
Nonconsensual sexual experiences (NSEs) may contribute to mental health concerns among incarcerated individuals, yet NSEs are understudied in this population. This study takes a novel approach in examining the prevalence of NSEs among incarcerated males by utilizing both quantitative and qualitative measures. The sample consisted of 189 men from three provincial maximum-security prisons in Ontario, Canada. Based on quantitative findings, 44.2% of the sample experienced NSEs before the age of 18, and 41.7% of the sample endorsed an experience that fit the legal definition of a NSEs as adults. Participants also responded to a qualitative open-ended question about their history of NSEs. Based on qualitative findings, a total of 23% of the men reported at least one incident of a NSE (e.g., child and adult). Based Findings highlight the high prevalence of NSEs among incarcerated men with quantitative responses demonstrating how the use of a behavioral questionnaire may, to some extent, correct for underreporting of NSEs. Qualitative responses illustrate the lived experience of incarcerated men and provide a deeper understanding of their NSEs. Responses also speak to the lack of resources and support available to these men. Findings underscore the need for proactive approaches in meeting mental health needs of incarcerated men in general and with regard to NSEs in particular. Results may inform the development of future correctional procedures (i.e., intake protocols that account for men with NSEs) and resources to support incarcerated men in navigating the psychological impact of non-consensual sexual experiences.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".