Complementary/alternative medicine: comparing the view of medical students with students in other health care professions.
Bibliographic record
Abstract
OBJECTIVE: We compared the opinions, knowledge, and attitudes of final-year medical, physiotherapy, occupational therapy, nursing, and pharmacy students about complementary/alternative medicine (CAM). METHODS: A cross-sectional study questionnaire (n = 442) was administered on site at the University of Western Ontario and the University of Toronto to fourth-year health professions students. Outcome measures were self-reported knowledge, attitude, and perceived usefulness of CAM therapies, the perceived importance of scientific inquiry for the acceptance of CAM, and educational exposure to the topic. RESULTS: Educational exposure to CAM was correlated with the perceived usefulness of CAM. Medical students reported the least amount of education about CAM and viewed CAM therapies as less useful than did their health professions student peers. Medical students and pharmacy students were more likely than the other health professions students to view traditional scientific forms of evidence as necessary before accepting CAM therapies. CONCLUSIONS: Perceptions differed among the different health professions student groups about the usefulness of CAM therapies and the kind of evidence needed before they should be incorporated into standard care. This may have important implications for multidisciplinary care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".