Characterising risk of homicide in a population-based cohort
Bibliographic record
Abstract
BACKGROUND: Homicide is an extreme expression of violence that has attracted less attention from public health researchers and policy makers interested in prevention. The purpose of this study was to examine the socioeconomic gradient of homicide and to determine whether risk differs by immigration status. METHODS: We conducted a population-based cohort study using linked vital statistics, census and population data sets that included all deaths by homicide from 1992 to 2012 in Ontario, Canada. We calculated age-adjusted death rates for homicide by material deprivation quintiles, stratified by immigration status. Count-based negative binomial regression models were used to calculate unadjusted and adjusted rate ratios with predictors of interest being age, urban residence, material deprivation and immigration status. A subanalysis containing immigrants only examined the effect of time since immigration and immigration class. RESULTS: There were 3345 homicide deaths registered between 1992 and 2012. Relative to low material deprivation areas, age-adjusted rates of homicide deaths in high materially deprived areas were similar among refugees (RR: 48.49; 95% CI 36.99 to 62.45) and long-term residents (RR: 47.67; 95% CI 44.66 to 50.83), but were slightly lower for non-refugee immigrants (RR: 38.53; 95% CI 32.42 to 45.45). Female refugees experienced a 1.31 (95% CI 0.88 to 1.94) higher rate and male refugees experienced a 1.23 (95% CI 0.90 to 1.67) higher rate of homicide victimisation compared with long-term residents. In an immigrant only analysis, the risk of homicide among refugees increased with duration of residence. CONCLUSIONS: Given the large area-level, socioeconomic status gradients observed in homicides among refugees, community-level and culturally appropriate prevention approaches are important.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".