Domestic violence and abuse related emergency room visits in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Hospitals' emergency rooms (ERs) are generally the first point of contact of domestic violence and abuse (DVA) victims to the health care system. For efficient management and resource allocation for ERs to manage DVA-related emergencies in Canada, it is important to quantify and assess the pattern of these visits. METHODS: Aggregate DVA-related ER visits data, using relevant ICD-10-CA codes, from 2012 to 2016 were retrieved from IntelliHealth Ontario. The 2011 ON-Marg (Ontario Marginalization) indices were linked at the Dissemination Area level to ER data. Descriptive analyses including total number and rate of visits per 100,000 people were calculated, stratified by age and sex. The Slope Index of Inequality (SII) and Relative Index of Inequality (RII) were also assessed. RESULTS: From 2012 to 2016, 10,935 (81.2% by females and 18.8% by males) DVA-related visits were made to ERs in Ontario. An annual average of 25.5 visits per 100,000 females and 6.1 visits per 100,000 males was observed. Residential instability and deprivation were significant predictors of DVA-related ER visits. No particular site of injury was indicated in 38.5% of visits, 24.7% presented with cranio-maxillofacial (CMF) trauma in isolation, 28.9% presented with non-CMF injuries, and 7.9% visits presented with both CMF and non-CMF injuries. CONCLUSION: This study identified that the burden of DVA-related ER visits is large enough to warrant timely public health interventions, and observed that certain populations in Ontario experience more DVA and/or are more prone to its impact. Our findings have important implications for various stakeholders involved in planning and implementing relevant policies and programs.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| 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".