Lowering the Threshold for Discussions of Domestic Violence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Women experiencing domestic violence (DV) frequent health care settings, but DV is rarely identified. METHODS: We conducted a randomized controlled trial to determine the effect of computer screening on health care provider-patient DV communication at 2 socioeconomically diverse emergency departments (EDs). Consenting nonemergent female patients, aged 18 to 65 years, were randomized to self-administered computer-based health risk assessment, with a prompt for the health care provider, or to "usual care"; all visits were audiotaped. Outcome measures were rates of DV discussion, disclosure, and services. RESULTS: Of 2169 eligible patients, 1281 (59%) consented; 871 (68%) were successfully audiotaped, and 903 (71%) completed an exit questionnaire. Rates of current DV risk on exit questionnaire were 26% in the urban ED and 21% in the suburban ED. In the urban ED, the computer prompt increased rates of DV discussion (147/262 [56%] vs 123/275 [45%]; P = .004), disclosure (37/262 [14%] vs 23/275 [8%]; P = .07), and services provided (21 [8%] vs 10 [4%]; P = .04). Women at the suburban site and those with private insurance or higher education were much less likely to be asked about experiences with abuse. Only 48% of encounters with a health care provider prompt regarding potential DV risk led to discussions. Both inquiries about and disclosures of abuse were associated with higher patient satisfaction with care. CONCLUSIONS: Computer screening for DV increased but did not guarantee that DV would be addressed during ED encounters. Nonetheless, it is likely that low-cost interventions that allow patients the opportunity to self-disclose can be used to improve detection of DV.
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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 | 0.001 |
| 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