Screening for Intimate Partner Violence in Orthopedic Patients
Bibliographic record
Abstract
Accurately identifying victims of intimate partner violence (IPV) can be a challenge for clinicians and clinical researchers. Multiple instruments have been developed and validated to identify IPV in patients presenting to health care practitioners, including the Woman Abuse Screening Tool (WAST) and the Partner Violence Screen (PVS). The purpose of the current study is to determine if female patients attending an outpatient orthopaedic fracture clinic who screen positive for IPV using three direct questions (direct questioning) also screen positive on the WAST and PVS. We conducted a prevalence study at two Level I trauma centres to determine the prevalence of IPV in female patients presenting to orthopaedic fracture clinics for treatment of injuries. We used three methods to determine the prevalence of IPV; 1) direct questioning, 2) WAST, and 3) PVS. We compared the prevalence rates across the three screening tools. Ninety-four women screened positive for IPV using any method. The prevalence of IPV was 30.5% when a direct questioning approach was utilized, 12.4% using the WAST, and 9.2% using the PVS. The WAST identified 37.2% (35/94) of the IPV victims detected and the PVS identified 27.7% (53/94) of the IPV victims detected, whereas direct questioning identified 89.4% of the IPV victims. Identification of IPV may be under-estimated by the WAST and PVS screening tools. Our findings suggest direct questioning may increase the frequency of disclosure of IPV among women attending outpatient orthopaedic clinics.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".