Chiropractors' Perceptions About Intimate Partner Violence: A Cross-Sectional Survey
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to assess chiropractors' attitudes, beliefs, knowledge, and experience about intimate partner violence (IPV). METHODS: This cross-sectional survey was developed by members of the Violence Against Women Health Research Collaborative. The survey was disseminated to a voluntary, nonrandom convenience sample of chiropractors attending a 3-day continuing education seminar. Surveys were distributed at the entrances of the seminar session rooms and placed on luncheon tables. Respondents returned surveys to collection boxes. RESULTS: Ninety-three doctors of chiropractic completed the survey. Respondents estimated that only 5.2% (95% confidence interval, 3.3%-7.0%) of their female patients were victims of IPV. General knowledge of IPV was good among respondents. Knowledge of clinical indicators and victim's management was fair to poor. Only 22% of respondents identified the most commonly injured body regions among battered women. Lack of knowledge, personal discomfort, and time constraints were all cited as barriers to IPV screening. CONCLUSIONS: Our survey indicates that doctors of chiropractic underestimate the prevalence of IPV among their female patients. Like other health care specialists, chiropractors cite multiple IPV screening barriers, especially lack of knowledge. Doctors of chiropractic would benefit from education and training in IPV to enable them to better identify and assist patients who are victims of IPV.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".