Feasibility of Screening for Intimate Partner Violence at Orthopedic Trauma Hospitals in India
Bibliographic record
Abstract
BACKGROUND: Intimate Partner Violence (IPV) involves behavior that causes physical, psychological, or sexual harm, and has significant health consequences. Given the prevalence of and impact of IPV, various organizations recommend routine IPV screening for women by health-care professionals. OBJECTIVE: We investigated the feasibility of screening women for IPV at a hospital in India. Specifically, we assessed prevalence of IPV, method of questionnaire administration, response rate, availability of IPV related community services for referrals, environment of screening, and explored perspectives of health professionals regarding in-hospital screening. STUDY DESIGN: We administered two questionnaires to consenting women; the composite abuse scale (CAS) and Woman Abuse Screening Tool (WAST). Health professionals involved in conducting the study and in managing care for patients were also interviewed. RESULTS: Forty-seven patients were enrolled in the study. The most reported injury was fractures (39% [CI 25%-54%]) and the greatest proportion involved spine and neck (28% [CI 16%-43%]). Prevalence of IPV was 30% [CI 17%-45%] according to the WAST and 40% [CI 26%-56%] according to the CAS. A majority of the participants used self-report as the method of questionnaire administration. Additionally, the self-report group had greater disclosure than the interview-administered group. The environment at this private hospital was considered adequate for screening and we found several IPV support networks in the community. However, health professionals were reluctant to screen for IPV. CONCLUSIONS: Our findings suggest that screening for IPV at an orthopaedic clinic in India is feasible.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".