Computer-assisted screening for intimate partner violence and control: a randomized trial
Why this work is in the frame
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Bibliographic record
Abstract
Background: Intimate partner violence and control (IPVC) is prevalent and can be a serious health risk to women. Objective: To assess whether computer-assisted screening can improve detection of women at risk for IPVC in a family practice setting. Design: Randomized trial. Randomization was computer-generated. Allocation was concealed by using opaque envelopes that recruiters opened after patient consent. Patients and providers, but not outcome assessors, were blinded to the study intervention. Setting: An urban, academic, hospital-affiliated family practice clinic in Toronto, Ontario, Canada. Participants: Adult women in a current or recent relationship. Intervention: Computer-based multirisk assessment report attached to the medical chart. The report was generated from information provided by participants before the physician visit (n = 144). Control participants received standard medical care (n = 149). Measurements: Initiation of discussion about risk for IPVC (discussion opportunity) and detection of women at risk based on review of audiotaped medical visits. Results: The overall prevalence of any type of violence or control was 22% (95% CI, 17% to 27%). In adjusted analyses based on complete cases (n = 282), the intervention increased opportunities to discuss IPVC (adjusted relative risk, 1.4 [CI, 1.1 to 1.9]) and increased detection of IPVC (adjusted relative risk, 2.0 [CI, 0.9 to 4.1]). Participants recognized the benefits of computer screening but had some concerns about privacy and interference with physician interactions. Limitation: The study was done at 1 clinic, and no measures of women's use of services or health outcomes were used. Conclusion: Computer screening effectively detected IPVC in a busy family medicine practice, and it was acceptable to patients. Primary Funding Source: Canadian Institutes of Health Research and Ontario Women's Health Council.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it