A 15-Year Population-Based Investigation of Sexual Assault Cases Across the Province of Ontario, Canada, 2002–2016
Bibliographic record
Abstract
Objectives. To estimate the population-level frequencies and standardized rates of sexual assault cases in the province of Ontario, Canada. Methods. We conducted a 15-year retrospective analysis (2002–2016) of sexual assault cases by linking 5 provincial administrative health databases. We defined sexual assault by an algorithm of 23 International Classification of Diseases, 10th Revision, and physician billing codes. We calculated age- and sex-stratified standardized rates per 100 000 census population, and we used age- and sex-stratified Poisson regressions to determine annual rate ratios. Results. Between 2002 and 2016, there were 52 780 incident cases of sexual assault in Ontario at a rate of 27.38 per 100 000 population. The highest rates were found among females aged 15 to 19 years (187 per 100 000) and 20 to 24 years (127 per 100 000). Among males, the highest rates were observed among children aged 0 to 4 years (41 per 100 000) and 5 to 9 years (29 per 10 000). Among males and females, the annual rate ratio increased among those aged 15 years and older and decreased among those aged 14 years and younger. Conclusions. Sexual assault was documented across all age groups and sexes, from children to elders, with high standardized rates among adolescents and children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".