The Frequency of Sexual Perpetration in College Men: A Systematic Review of Reported Prevalence Rates From 2000 to 2017
Bibliographic record
Abstract
BACKGROUND: The prevalence of sexual perpetration in college men is unknown. Prior reviews of sexual violence prevalence rates have produced wide-ranging estimates, likely due to wide variation in measurement strategies. OBJECTIVES: This article systematically reviewed research findings (from 2000 to 2017) regarding prevalence rates of sexual perpetration in college men and measurement strategies. Data Sources: PsycINFO and Web of Science databases. Eligibility Criteria: Empirical reports published between 2000 and 2017 that included male participants, available in English, and reported lifetime prevalence findings in Canadian or American college students. Participants: Data from 78 independent samples including 25,524 college men. RESULTS: = 6.3). Studies that used non-Sexual Experiences Survey (SES)-based questionnaires recorded higher prevalence rates (41.5%) than SES-using studies (26.2%). At least 16 different sexual perpetration questionnaires were identified. Modifying standardized questionnaires was extremely common; this was reported in over half of the studies. Studies using modified standardized questionnaires found higher prevalence rates of sexual perpetration than studies using nonmodified standardized questionnaires. LIMITATIONS: This report focused exclusively on college men in the United States and Canada. CONCLUSIONS AND IMPLICATIONS: On average, 29% of college males report engaging in behaviors defined as sexual perpetration; however, there was a strong influence of measurement strategy on reported rates.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".