Complementary and alternative medicine: Do physicians believe they can meet the requirements of the Collège des médecins du Québec?
Bibliographic record
Abstract
OBJECTIVE: To determine whether medical training prepares FPs to meet the requirements of the Collège des médecins du Québec for their role in advising patients on the use of complementary and alternative medicine (CAM). DESIGN: Secondary analysis of survey results. SETTING: Quebec. PARTICIPANTS: Family physicians and GPs in active practice. MAIN OUTCOME MEASURES: Perceptions of the role of the physician as an advisor on CAM; level of comfort responding to questions and advising patients on CAM; frequency with which patients ask their physicians about CAM; personal position on CAM; and desire for training on CAM. RESULTS: The response rate was 19.5% (195 respondents of 1000) and the sample appears to be representative of the target population. Most respondents (85.8%) reported being asked about CAM several times a month. A similar proportion (86.7%) believed it was their role to advise patients on CAM. However, of this group, only 33.1% reported being able to do so. There is an association between an urban practice and knowledge of the advisory role of physicians. More than three-quarters of respondents expressed interest in receiving additional training on CAM. CONCLUSION: There is a gap between the training that Quebec physicians receive on CAM and their need to meet legal and ethical obligations designed to protect the public where CAM products and therapies are concerned. One solution might be more thorough training on CAM to help physicians meet the Collège des médecins du Québec requirements.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".