Intimate Partner Violence: Development of a Brief Risk Assessment for the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Women assaulted by intimate partners are frequently patients in emergency departments (EDs). Many victims and health care providers fail to take into account the potential risks of repeat partner violence. The objective of this study was to use data from a larger study of domestic violence risk assessment methods to develop a brief assessment for acute care settings to identify victims at highest risk for suffering severe injury or potentially lethal assault by an intimate partner or former partner. METHODS: Victims of intimate partner violence (IPV) were interviewed twice between 2002 and 2004. The baseline interview included the 20 items of Campbell's Danger Assessment (DA; predictor). The follow-up interview, conducted 9 months later on average, assessed abuse inflicted since the baseline interview (outcome). Multiple logistic regression was used to identify questions on the DA most predictive of severe abuse and potentially lethal assaults. Female IPV victims were recruited from New York City family courts, Los Angeles County Sheriff's Department 9-1-1 calls, New York City and Los Angeles shelters, and New York City hospitals; 666 women responded to the DA at baseline, and 60% participated in follow-up interviews. RESULTS: Severe injuries or potentially lethal assaults were experienced by 14.9% of retained study participants between the baseline and follow-up interviews. The best brief prediction instrument has five questions. A positive answer to any three questions has a sensitivity of 83% (95% confidence interval = 70.6% to 91.4%). CONCLUSIONS: This instrument can help predict which victims may be at increased risk for severe injury or potentially lethal assault and can aid clinicians in differentiating which patients require comprehensive safety interventions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".