A national approach to teaching complementary and alternative medicine in Canadian medical schools: The CAM in UME project.
Bibliographic record
Abstract
Complementary and Alternative Medicine (CAM) is increasingly used in North America. Most patients combine conventional and CAM treatments, however, The widespread use of CAM is not without concern. The growing use of CAM is drawing more and more attention from individuals involved in medical education. We present an overview of a national initiative to integrate CAM in undergraduate medical education (UME) programs in Canada. The aim of this initiative, called the CAM in UME Project, is to facilitate high quality and balanced teaching of CAM related issues in UME. The project's activities include the development of (1) CAM-oriented competencies, (2) peer-reviewed summaries of topics relevant to CAM, (3) a searchable repository of teaching and learning resources, and (4) a guide for the development, implementation and sustainability of CAM curriculum. All these are housed on the CAM in UME Web site (http://www.caminume.ca) which is freely accessible to interested individuals. It appears that teaching students about natural health products, an important component of CAM, would be a natural fit for pharmacologists based at 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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".