Emergency Medical Services: a resource for victims of domestic violence?
Bibliographic record
Abstract
BACKGROUND: Domestic violence (DV), also known as intimate partner violence (IPV), is one of the leading causes of serious injury among women of childbearing age. As first responders on the scene during DV calls where personal injuries have occurred, Emergency Medical Services (EMS) could routinely identify, report and assist victims of violence. Yet, little is known of the prevalence of DV calls in EMS practice, Emergency Medical Technicians' (EMT) knowledge and comfort in responding to such calls, or how they care for victims. METHOD: The objectives of this study were to assess EMTs' knowledge of and experience with providing care to victims of DV in the province of Ontario, Canada. Data were gathered through an online, short-answer survey. Survey data were analysed using basic frequency displays, and descriptive statistics are reported. RESULTS: Almost 500 EMTs participated in this study, the vast majority of whom (90%) attended at least one DV call in the preceding year, with 65% attending between 10 and 20 DV calls. The majority of respondents (84.5%) wished for more education and training on the issue. CONCLUSION: EMTs have frequent contact with victims of DV yet have received little education about the issue. The majority of those surveyed would like specific education and training on DV.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.002 |
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".