Intimate partner violence (IPV) in male and female orthopaedic trauma patients: a multicentre, cross-sectional prevalence study
Bibliographic record
Abstract
OBJECTIVES: Identify the proportion of patients attending fracture clinics who had suffered intimate partner violence (IPV) within the past year. DESIGN: Powered cross-sectional study using validated participant self-reported questionnaires. SETTING AND PARTICIPANTS: Adult trauma patients (no gender/age exclusions) attending one of three Scottish adult fracture clinics over 16-month period (from October 2016 to January 2018). PRIMARY OUTCOME MEASURE: Number of participants answering 'yes' to the Woman Abuse Screening Tool question: 'In your current relationship over the past twelve months, has your partner ever abused you physically/emotionally/sexually?' RESULTS: Of 336 respondents, 46% (156/336 known) were women with 65% aged over 40 (212/328 known). The overall prevalence of IPV within the preceding 12 months was 12% 39/336) for both male and female patients. The lifetime prevalence of IPV among respondents was 20% (68/336). 38% of patients who had experienced IPV within the past 12 months had been physically abused (11/29). None of the patients were being seen for an injury caused by abuse. Two-thirds of respondents thought that staff should ask routinely about IPV (55%, 217/336), but only 5% had previously been asked about abuse (18/336). CONCLUSIONS: This is the first study worldwide investigating the prevalence of IPV in fracture clinics for both male and female patients. 12-month prevalence of IPV in fracture clinic patients is significant and not affected by gender in this study. Patients appear willing to disclose abuse within this setting and are supportive of staff asking about abuse. This presents an opportunity to identify those at risk within this vulnerable population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".