Developing a National Vision for Complementary and Alternative Medicine in Undergraduate Medical Education: Report on an Invitational Workshop
Bibliographic record
Abstract
The increasing use of Complementary and Alternative Medicine (CAM) among Canadians has led to exploring the potential role of CAM in undergraduate medical education (UME). As part of a multi-phased project to begin developing CAM curriculum for UME, representatives of 14 Canadian medical schools attended a two day workshop in September 2003 in Saskatoon, SK. Workshop outcomes included developing (1) a rationale for CAM in UME, (2) draft student learning objectives, (3) a list of potential CAM curriculum topics, and (4) a list of factors affecting curriculum implementation. Much of the discussions centred on what Canadas future physicians need to know about CAM. Broad curricular categories included (1) general CAM topics (e.g., definitions, utilization, reasons for use, evidence, implications for practice, bridging paradigms), and (2) specific CAM products and practices. The top rated practice areas for physicians to know about were Natural Health Products, Traditional Chinese Medicine, chiropractic, naturopathy and homeopathy. Participants identified that for each of these practices/products a descriptive overview, information about evidence, safety and regulation, and clinical implications needs to be discussed. Using the workshop recommendations as a foundation, the CAM in UME project team continues to develop a national, consensus-based CAM curriculum for Canadian medical schools.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".