Bibliographic record
Abstract
Background: The past decades have occasioned an explosion of research on Domestic Violence and the health care response. It has become clear that abused women are frequently seen in emergency departments, yet despite the research, the prevalence of the issue among patients, its serious health consequences, and the need for training acknowledged by numerous medical organizations, there is no standardized curriculum for training health care providers, nor an articulated set of competencies to guide curricular development. Objectives: To develop evidence-based competencies on Domestic Violence relevant to health care providers, particularly those in emergency department settings. Methods: Following a modified Delphi process, we completed a literature review for the years 2001-2006 to determine evidence-based practices. Next, an expert panel extracted relevant competencies from the reviewed literature. The competencies were confirmed through consultation with 66 stakeholders across the province of Ontario. Results: Forty-four respondents provided concrete feedback on the competencies, confirming their importance and validity. Conclusion: This paper describes a comprehensive methodological approach to the challenge of developing competencies in DV relevant to health care providers practicing in emergency department settings. The development of competencies is an important first step in the development of a common, standardized, evidence-based medical curriculum.
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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 | 0.144 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".