Geographies of Sexual Assault: A Spatial Analyses to Identify Neighborhoods Affected by Sexual and Gender-Based Violence
Bibliographic record
Abstract
Emergency departments are a common access point for survivors of sexual and gender-based violence (SGBV), but very little is known about where survivors live and the neighborhoods they return to. The objectives of this study were to describe the patient population that present for a sexual or partner-based assault and explore the geographic distribution of cases across the Ottawa-Gatineau area. Data for this study were extracted from the Sexual Assault and Partner Abuse Care Program (SAPACP) case registry (January 1 to December 31, 2015) at The Ottawa Hospital. Spatial analyses were conducted using six-digit postal codes converted into Canadian Census Tract units to identify geographic areas with concentrated cases of SGBV. Concentrated areas were defined as Census Tracts with seven or more SGBV cases within a single calendar year. In 2015, there were 406 patients seen at the SAPACP and 348 had valid postal codes and were included in the analyses. More than 90% of patients were female and 152 (43.68%) were below 24 years of age. More than 70% knew their assailant and the most common locations of the assault were at the survivors' home (31.03%), assailants' home (27.01%), or outdoors (10.92%). Eight concentrated areas were identified including three in the downtown entertainment district, three lower income areas, one high-income neighborhood, and one suburb more than 20 km from downtown. The findings from this study describe the typical clinical presentation of sexual and domestic assault survivors and also challenge geographic stereotypes of where survivors live and what areas of the city are most affected by SGBV. Using residential information provides a survivor-centric approach that highlights the widespread nature of SGBV and supports the need for population-based approaches to improve care for survivors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".