Characteristics associated with sexual assaults at mass gatherings
Bibliographic record
Abstract
INTRODUCTION: Sexual assault is disturbingly common, yet little is known about those occurring at mass gatherings, defined as a group of people congregated for a common purpose. Our objectives were to examine patterns of variation in sexual assault associated with mass gatherings and to determine factors associated with assaults occurring at mass gatherings. METHODS: We performed a case series analysis from January to December, 2013. We included all patients >16 years presenting within 30 days of their sexual assault to the Ottawa Hospital Sexual Assault and Partner Abuse Care Program (SAPACP). Cases were stratified by whether or not they occurred at mass gatherings. We abstracted from the SAPACP records: patient and sexual assault characteristics, alcohol or drug consumption and medical and forensic care accepted. We performed descriptive analyses and multiple logistical regression to identify factors associated with mass gathering assaults. RESULTS: We found 204 cases of sexual assault, of which 53 (26%) occurred at mass gatherings. Relative frequencies of mass gathering sexual assaults peaked during New Year's Eve, Canada Day, university frosh week and Halloween. We found the following factors were statistically significantly associated with sexual assault at mass gatherings: younger age (OR=0.95, 95% CI 0.91 to 0.99); voluntary consumption of drugs and alcohol (3.88, 95% CI 1.34 to 11.23); assault occurring on a holiday (2.37, 95% CI 1.00 to 5.64) and the assailant unknown to the victim (2.43, 95% CI 1.15 to 5). INTERPRETATION: This study is the first to describe patterns of variation in sexual assault incidents associated with occurrence of mass gatherings as well as factors associated with such assaults. We will disseminate these results to key stakeholders in order to develop prevention-minded policies for future mass gatherings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".