Paramedic Identification and Management of Victims of Intimate Partner Violence: A Literature Review
Bibliographic record
Abstract
Introduction Intimate partner violence (IPV) occurs between adults of the same or opposite sex in a current, or past, intimate relationship. The aim of this paper is to review the literature regarding paramedic confidence, capacity and accuracy when identifying adult victims of IPV and subsequent management of the scene when IPV is suspected or identified. Methods A review of the literature using Ovid MEDLINE was conducted; five articles met the inclusion and exclusion criteria. Results Results show a consistency in findings across research areas in Australia, Canada and the United States and are clear in four separate areas: paramedics demonstrate a high degree of accuracy in identifying IPV victims; professional training effectively increases paramedic knowledge of IPV; greater than 50% of the paramedic population surveyed felt underprepared to deal with an IPV scene; and the majority of surveyed paramedics attend between one and 10 IPV scenes per year. Conclusion This review indicates that paramedics have the capacity to accurately identify IPV victims, and that paramedics recognise a deficit in their professional IPV training. Further research is required, using a robust sample size, to construct appropriate training packages and guide improvement to paramedic clinical practice guidelines.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".