The importance of health advocacy in Canadian postgraduate medical education: current attitudes and issues
Bibliographic record
Abstract
BACKGROUND: Health advocacy is currently a key component of medical education in North America. In Canada, Health Advocate is one of the seven roles included in the Royal College of Physicians and Surgeons of Canada's CanMEDS competency framework. METHOD: A literature search was undertaken to determine the current state of health advocacy in Canadian postgraduate medical education and to identify issues facing educators and learners with regards to health advocacy training. RESULTS: The literature revealed that the Health Advocate role is considered among the least relevant to clinical practice by educators and learners and among the most challenging to teach and assess. Furthermore learners feel their educational needs are not being met in this area. A number of key barriers affecting health advocacy education were identified including limited published material on the subject, lack of clarity within the role, insufficient explicit role modeling in practice, and lack of a gold standard for assessment. Health advocacy is defined and its importance to medical practice is highlighted, using pediatric emergency medicine as an example. CONCLUSIONS: Increased published literature and awareness of the role, along with integration of the new 2015 CanMEDS framework, are important going forward to address concerns regarding the quality of postgraduate health advocacy education in Canada.
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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 | 0.033 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".