Computer-Assisted Screening for Intimate Partner Violence and Control
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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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".