Canadian Family Physicians and Complementary/Alternative Medicine: The Role of Practice Setting, Medical Training, and Province of Practice<sup>*</sup>
Bibliographic record
Abstract
Cette étude jette une certaine lumière sur la façon selon laquelle les médecins de famille canadiens offrent des services de médecine douce et complémentaire (MDC) à leurs patients, et pourquoi ils le font. Les résultats des recherches des auteurs démontrent que les environnements organisationnels découragent les médecins d'offrir des services de MDC alors que les cliniques indépendantes y sont plus favorables. Les médecins formés dans les facultés de médecine francophones sont moins susceptibles que leurs collègues formés en anglais d'offrir de tels services, et ceux de Colombie‐Britannique sont les plus portés à le faire. Les différences interprovinciales ne semblent pas liées à la présence ou à l'absence de législation de « preuve négative », qui est considérée faciliter la fourniture de ces services par les médecins. The present study sheds some light on how and why Canadian family physicians offer complementary and alternative medicine (CAM) services to their patients. Our results suggest that organizational settings discourage physicians from offering CAM, while solo clinics are most conducive. Physicians trained in French‐language medical schools are less likely than their English‐language trained colleagues to offer CAM services, and those in British Columbia are the most likely to do so. Provincial differences do not appear to be related to the presence or absence of “negative proof” legislation that is considered to facilitate CAM provision by physicians.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".