Approaches to Screening for Intimate Partner Violence in Health Care Settings
Bibliographic record
Abstract
CONTEXT: Screening for intimate partner violence (IPV) in health care settings has been recommended by some professional organizations, although there is limited information regarding the accuracy, acceptability, and completeness of different screening methods and instruments. OBJECTIVE: To determine the optimal method for IPV screening in health care settings. DESIGN AND SETTING: Cluster randomized trial conducted from May 2004 to January 2005 at 2 each of emergency departments, family practices, and women's health clinics in Ontario, Canada. PARTICIPANTS: English-speaking women aged 18 to 64 years who were well enough to participate and could be seen individually were eligible. Of 2602 eligible women, 141 (5%) refused participation. INTERVENTION: Participants were randomized by clinic day or shift to 1 of 3 screening approaches: a face-to-face interview with a health care provider (physician or nurse), written self-completed questionnaire, and computer-based self-completed questionnaire. Two screening instruments-the Partner Violence Screen (PVS) and the Woman Abuse Screening Tool (WAST)-were administered and compared with the Composite Abuse Scale (CAS) as the criterion standard. MAIN OUTCOME MEASURES: The approaches were evaluated on prevalence, extent of missing data, and participant preference. Agreement between the screening instruments and the CAS was examined. RESULTS: The 12-month prevalence of IPV ranged from 4.1% to 17.7%, depending on screening method, instrument, and health care setting. Although no statistically significant main effects on prevalence were found for method or screening instrument, a significant interaction between method and instrument was found: prevalence was lower on the written WAST vs other combinations. The face-to-face approach was least preferred by participants. The WAST and the written format yielded significantly less missing data than the PVS and other methods. The PVS and WAST had similar sensitivities (49.2% and 47.0%, respectively) and specificities (93.7% and 95.6%, respectively). CONCLUSIONS: In screening for IPV, women preferred self-completed approaches over face-to-face questioning; computer-based screening did not increase prevalence; and written screens had fewest missing data. These are important considerations for both clinical and research efforts in IPV screening. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00336297.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".