Effect of domestic violence training: systematic review of randomized controlled trials.
Bibliographic record
Abstract
OBJECTIVE: To describe and evaluate the effectiveness of domestic violence education in improving physicians' knowledge, recognition, and management of abused women. DATA SOURCES: The Cochrane Database of Systematic Reviews, MEDLINE, PubMed, PsycINFO, ERIC, and EMBASE were searched for articles published between January 1, 2000, and November 1, 2012. This search was supplemented by manual searches for relevant articles using a combined text-word and MeSH-heading search strategy. STUDY SELECTION: Randomized controlled trials were selected that used educational interventions among physicians and provided data on the effects of the interventions. SYNTHESIS: Nine randomized controlled trials were included that described different educational approaches with various outcome measures. Three studies examined the effects of educational interventions among postgraduate trainee physicians and found an increase in knowledge but no change in behaviour with regard to identifying victims of domestic violence. Six studies examined educational interventions for practising physicians. Three of these studies used multifaceted physician training that combined education with system support interventions to change physician behaviour, such as increasing general awareness of domestic violence with brochures and posters, providing aids to remind physicians how to identify victims, facilitating physician access to victim support services, and providing audits and feedback. Multifaceted educational interventions included interactive workshops, Web-based learning, and experiential training. Another study used focus-group discussions and training, and showed improved domestic violence reporting among physicians. The remaining 2 studies showed improved perceptions of practising physicians' self-efficacy using problem-based online learning. CONCLUSION: It was difficult to determine the most effective educational strategy, as the educational interventions and the outcome measures varied among the selected studies. Brief interventions for postgraduate trainee physicians improved knowledge but did not seem to affect behaviour. Online education using a problem-based learning format improved practising physicians' perceptions, knowledge, and skills in managing domestic violence. Physician training combined with system support interventions seemed to benefit domestic violence victims and increase referrals to domestic violence support resources.
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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 | 0.031 | 0.136 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".