A scoping review of intimate partner violence educational programs for health care professionals
Bibliographic record
Abstract
Multiple intimate partner violence (IPV) educational programs have been developed for health care professionals (HCPs); however, program content and effectiveness vary substantially. The purpose of this scoping review was to identify and synthesize the literature evaluating IPV education programs for HCPs to identify key areas for potential evidence-based recommendations and focus future research priorities. We conducted a systematic literature search using broad eligibility criteria to identify studies published between January 2000 and July 2015 that evaluated the effectiveness of IPV education programs in health care settings. All potentially eligible references were screened independently by two reviewers. Data extraction was completed independently by two reviewers for all eligible studies. Descriptive statistics were used to summarize all data. We identified 65 eligible studies, 55% of which reported positive program effectiveness. Effective programs often reported the use of online training components, delivery by an IPV educator/expert or physician/surgeon, the inclusion of a treatment protocol and resources for patients and HCPs, and included more than five training sessions lasting no more than one hours each. Our results demonstrate that IPV educational programs are heterogeneous and that a wide variety of methodologies have been used to evaluate their effectiveness.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".