“You're Not a Victim of Domestic Violence, Are You?” Provider–Patient Communication about 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 who are victims of domestic violence frequently seek care in an emergency department. However, it is challenging to hold sensitive conversations in this environment. OBJECTIVE: To describe communication about domestic violence between emergency providers and female patients. DESIGN: Analysis of audiotapes made during a randomized, controlled trial of computerized screening for domestic violence. SETTING: 2 socioeconomically diverse emergency departments: one urban and academic, the other suburban and community-based. PARTICIPANTS: 1281 English-speaking women age 16 to 69 years and 80 providers (30 attending physicians, 46 residents, and 4 nurse practitioners). RESULTS: 871 audiotapes, including 293 that included provider screening for domestic violence, were analyzed. Providers typically asked about domestic violence in a perfunctory manner during the social history. Provider communication behaviors associated with women disclosing abuse included probing (defined as asking > or =1 additional topically related question), providing open-ended opportunities to talk, and being generally responsive to patient clues (any mention of a psychosocial issue). Chart documentation of domestic violence was present in one third of cases. LIMITATIONS: Nonverbal communication was not examined. Providers were aware that they were being audiotaped and may have tried to perform their best. CONCLUSION: Although hectic clinical environments present many obstacles to meaningful discussions about domestic violence, several provider communication behaviors seemed to facilitate patient disclosure of experiences with abuse. Illustrative examples highlight common pitfalls and exemplary practices in screening for abuse and response to disclosures of abuse.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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