Factors associated with experiencing reassault in Ontario, Canada: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Individuals who experience a violence-related injury are at high risk for subsequent assault. The extent to which characteristics of initial assault are associated with the risk and intensity of reassaults is not well described yet essential for planning preventive interventions. We sought to describe the incidence of reassault and associated risk factors in Ontario, Canada. METHODS: In this population-based retrospective cohort study using linked health and demographic administrative databases, we included all individuals discharged from an emergency department or hospitalised with a physical assault between 1 April 2005 and 30 November 2016 and followed them until 31 December 2016 for reassault. A sex-stratified Andersen-Gill recurrent events analysis modelled associations between sociodemographic and clinical risk factors and reassault. RESULTS: 271 522 individuals experienced assault (mean follow-up=6.4 years), 24 568 (9.0%) of whom were reassaulted within 1 year, 45 834 (16.9%) within 5 years and 52 623 (19.4%) within 10 years. 40 322 (21%) males and 12 662 (17%) females experienced reassault over the study period. Groups with increased rates of reassault included: those aged 13-17 years versus older adults (age 65+) (males: relative rate (RR) 2.16; 95% CI 1.96 to 2.38; females: RR 2.79; 95% CI 2.39 to 3.26)), those living in rural areas versus urban (males: RR 1.22; 95% CI 1.19 to 1.24; females: RR 1.32; 95% CI 1.27 to 1.37) and individuals with a history of incarceration versus without (males: RR 2.38; 95% CI 2.33 to 2.42; females: RR 2.57; 95% CI 2.48 to 2.67). CONCLUSION: One in five who are assaulted experience reassault. Those at greatest risk include youth, those living in rural areas, and those who have been incarcerated, with strongest associations among females. Timely interventions to reduce the risk of experiencing reassault must consider both sexes in these groups.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".