“You're Not a Victim of Domestic Violence, Are You?” Provider–Patient Communication about Domestic Violence
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".