Self-report, medical staff interview, and physician interview had similar effectiveness for screening for domestic violence in womenCommentary
Bibliographic record
Abstract
P H Chen Dr P H Chen, University of Medicine and Dentistry of New Jersey–New Jersey Medical School. Newark, NJ, USA; chenpi@umdnj.edu What is the relative effectiveness of self-report, medical staff interview, and physician interview for screening for domestic violence (DV) in women? ### Design: randomised controlled trial. ### Allocation: {concealed}.* ### Blinding: {unblinded}.* ### Follow-up period: end of healthcare visit. ### Setting: 4 family practices {in the US}.* ### Patients: 523 women ⩾18 years of age (mean age 36 y, 71% black) who were currently living with a partner. ### Intervention: self-report (n = 173), medical staff {included nurses and medical assistants}* interview (n = 169), or physician interview (n = 181) for administering 2 questionnaires to screen for DV: Woman Abuse Screening Tool (WAST)-Short and Hurt-Insult-Threaten-Scream (HITS). WAST-Short had 2 questions (“In general, how would you describe your relationship? A …
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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 | 0.032 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".