Associations between small-area sociodemographic characteristics and intimate partner violence in Montréal, Québec
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) affects many individuals and can have a significant impact on their health and well-being. In order to inform prevention strategies, several studies have focused on the determinants of IPV. However, knowledge on the association between neighbourhood characteristics and IPV remains scarce. The social disorganization theory posits that certain neighbourhood characteristics are associated with violent behaviours. This theory has been used to explain spatial variations in IPV, but most studies have been conducted in the United States. Little is known about the effect of neighbourhood factors in urban contexts outside of the United States. Design and methods: Using police data from 2016 and 2017, this study estimated the association between sociodemographic characteristics of neighbourhoods (socioeconomic status, single-parenthood, residential instability and ethnocultural heterogeneity) and IPV victimization in Montréal, Québec. Results: Results suggest a neighbourhood-level variation in IPV, and that neighbourhood-level characteristics are associated with IPV victimization. Specifically, the likelihood of IPV is higher in neighbourhoods with the lowest SES level (OR = 2.80, 95%CI: 2.47–3.17, p < 0.001) and the lowest level of residential instability (OR = 0.81, 95%CI: 0.70–0.93, p = 0.003) as well as the highest proportion of single-parent households (OR = 1.88, 95%CI: 1.65–2.15, p < 0.001). Conclusion: Although neighbourhood-level interventions to reduce IPV are rare, our results highlight the importance of developing such preventive strategies. Prevention programs targeting high-risk neighbourhoods may prove effective in reducing IPV.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".