Achieving just outcomes: forensic evidence collection in emergency department sexual assault cases
Bibliographic record
Abstract
BACKGROUND: Achieving just outcomes in sexual assault cases is one of the most serious and complex problems facing the healthcare and justice systems. This study was designed to determine the prevalence and correlates of Sexual Assault Evidence Kit (SAEK) completion and release to police among sexual assault cases presenting to the ED. METHODS: Data for this retrospective study come from the Sexual Assault and Partner Abuse Care Programme (SAPACP) case registry (1 January to 31 December, 2015) at The Ottawa Hospital, a unique medical-forensic access point and the only facility offering SAEK collection in Ottawa. Bivariable and multivariable logistic regression models were conducted using ORs, adjusted ORs (AORs) and 95% CIs. RESULTS: In 2015, 406 patients were seen by the SAPACP and 202 (77.1%) were eligible for a SAEK. Among eligible cases, 129 (63.9%) completed a SAEK and 60 (29.7%) released the SAEK to police for investigation. Youth cases (≤24 years) had the highest odds of completing a SAEK (AOR 2.23, 95% CI 1.18 to 4.23). Cases who were uncertain of the assailant (AOR 3.62, 95% CI 1.23 to 10.67) and assaults that occurred outdoors (AOR 3.14, 95% CI 1.08 to 9.09) were most likely to release the SAEK to police. CONCLUSION: Even with access to specialised forensic evidence collection, many sexual assault survivors do not complete a SAEK, and even fewer release the evidence to police for investigation. The ED is a common entry points into the healthcare system, and this study has highlighted the need to strengthen services and reduce attrition along the health-justice continuum.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".