Lowering the Threshold for Discussions of Domestic Violence
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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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".