Are clinicians being prepared to care for abused women? A survey of health professional education in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The current project undertook a province-wide survey and environmental scan of educational opportunities available to future health care providers on the topic of intimate partner violence (IPV) against women. METHODS: A team of experts identified university and college programs in Ontario, Canada as potential providers of IPV education to students in health care professions at the undergraduate and post-graduate levels. A telephone survey with contacts representing these programs was conducted between October 2005 and March 2006. The survey asked whether IPV-specific education was provided to learners, and if so, how and by whom. RESULTS: In total, 222 eligible programs in dentistry, medicine, nursing and other allied health professions were surveyed, and 95% (212/222) of programs responded. Of these, 57% reported offering some form of IPV-specific education, with undergraduate nursing (83%) and allied health (82%) programs having the highest rates. Fewer than half of undergraduate medical (43%) and dentistry (46%) programs offered IPV content. Postgraduate programs ranged from no IPV content provision (dentistry) to 41% offering content (nursing). CONCLUSION: Significant variability exists across program areas regarding the methods for IPV education, its delivery and evaluation. The results of this project highlight that expectations for an active and consistent response by health care professionals to women experiencing the effects of violence may not match the realities of professional preparation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".