Orthopaedic Surgeons’ Knowledge and Misconceptions in the Identification of Intimate Partner Violence Against Women
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV)-physical, sexual, psychologic, or financial abuse between intimate partners-is the most common cause of nonfatal injury to women in North America. As many IPV-related injuries are musculoskeletal, orthopaedic surgeons are well positioned to identify and assist these patients. However, data are lacking regarding surgeons' knowledge of the prevalence of IPV in orthopaedic practices, surgeons' screening and management methods, and surgeons' perceptions about IPV. QUESTIONS/PURPOSES: We aimed to identify (1) surgeon attitudes and beliefs regarding victims of IPV and batterers and (2) perceptions of surgeons regarding their role in identifying and assisting victims of IPV. METHODS: We surveyed 690 surgeon members of the Orthopaedic Trauma Association. The survey had three sections: (1) general perception of orthopaedic surgeons regarding IPV; (2) perceptions of orthopaedic surgeons regarding victims and batterers; and (3) orthopaedic relevance of IPV. One hundred fifty-three surgeons responded (22%). RESULTS: Respondents manifested key misconceptions: (1) victims must be getting something out of the abusive relationships (16%); (2) some women have personalities that cause the abuse (20%); and (3) the battering would stop if the batterer quit abusing alcohol (40%). In the past year, approximately ½ the respondents (51%) acknowledged identifying a victim of IPV; however, only 4% of respondents currently screen injured female patients for IPV. Surgeons expressed concerns regarding lack of knowledge in the management of abused women (30%). CONCLUSION: Orthopaedic surgeons had several misconceptions about victims of IPV and batterers. Targeted educational programs on IPV are needed for surgeons routinely caring for injured women.
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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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".