Spatial Disparities of Sexual and Gender-Based Violence Emergency Room Use in Canada: A Rural Perspective
Bibliographic record
Abstract
Sexual- and gender-based violence (SGBV) remains a widespread public health crisis, with women in rural, remote, and northern communities across Canada disproportionately impacted. Despite well-documented barriers to accessing services in rural areas, there is limited national data on hospital-based care for SGBV-related injuries. This study utilizes the 2016 Canadian Community Health and Environment Cohort, hospital administrative records, and the Index of Remoteness to examine emergency room use (ERU) for SGBV across urban and rural regions between 2015 and 2021, including the period of the COVID-19 pandemic. Stratified analyses were performed by SGBV subtype, remoteness level, and sociodemographic factors. Results consistently showed higher odds of SGBV-related ERU in rural areas for all subtypes (i.e., physical violence, intimate partner violence (IPV), and sexual violence, with rural residents 65% more likely to seek ER care for overall SGBV than their urban counterparts. ERU rates increased with remoteness, notably spiking in the most isolated regions. During COVID-19, ERU for sexual and physical violence grew in both urban and rural settings, while ERU related to IPV declined, indicating disrupted help-seeking during lockdowns. Sociodemographic analysis identified higher ERU among younger individuals, women, immigrants, economically marginalized groups, and those living in rental or subsidized housing in rural areas. By applying an intersectional geographic framework, this study sheds light on how geographic isolation, social inequities, and structural barriers influence emergency service use for SGBV-related injuries. The findings underscore the pressing need to invest in culturally safe, community-based alternatives to emergency care and to tailor intervention strategies that address the unique needs of rural populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".