Validation and adaptation of the danger assessment‐5: A brief intimate partner violence risk assessment
Bibliographic record
Abstract
AIMS: The aim of this study was to assess the predictive validity of the DA-5 with the addition of a strangulation item in evaluating the risk of an intimate partner violence (IPV) victim being nearly killed by an intimate partner. BACKGROUND: The DA-5 was developed as a short form of the Danger Assessment for use in healthcare settings, including emergency and urgent care settings. Analyzing data from a sample of IPV survivors who had called the police for domestic violence, the DA-5 was tested with and without an item on strangulation, a potentially fatal and medically damaging IPV tactic used commonly by dangerous abusers. DESIGN: Researchers interviewed a heterogeneous sample of 1,081 women recruited by police between 2009-2013 at the scene of a domestic violence call; 619 (57.3%) were contacted and re-interviewed after an average of 7 months. METHODS: The predictive validity of the DA-5 was assessed for the outcome of severe or near lethal IPV re-assault using sensitivity, specificity and ROC curve analysis techniques. RESULTS: The original DA-5 was found to be accurate (AUC = .68), equally accurate with the strangulation item from the original DA substituted (AUC = .68) and slightly more accurate (but not a statistically significant difference) if multiple strangulation is assessed. CONCLUSION: We recommend that the DA-5 with the strangulation item be used for a quick assessment of homicide or near homicide risk among IPV survivors. A protocol for immediate referral and examination for further injury from strangulation should be adopted for IPV survivors at high risk.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".