Medical students’ attitudes to complementary and alternative medicine: Further validation of the IMAQ and findings from an international longitudinal study
Bibliographic record
Abstract
BACKGROUND: Current research mainly employs cross-sectional designs to examine changes in medical students' attitudes towards complementary and alternative medicine (CAM). AIMS: This paper reports the findings of a longitudinal study to further validate the Integrative Medicine Attitude Questionnaire (IMAQ) and examine changes in medical students' attitudes over 3 years. METHODS: A total of 154 medical students from four schools in three countries completed a modified version of the IMAQ during their first (T1) and fourth year (T2). RESULTS: We established the validity of a three-factor model for the IMAQ: (1) attitudes towards holism; (2) attitudes towards the effectiveness of CAM therapies, and (3) attitudes towards introspection and the doctor-patient relationship. We found that IMAQ factor scores did not differ significantly from T1 to T2, emphasizing the relative stability in attitudes across time. Various student characteristics were significantly associated with IMAQ factor scores at T2: age, gender, CAM use, CAM education and school; and two variables (gender and CAM use) predicted changes in medical students' attitudes between T1 and T2. CONCLUSIONS: We urge medical educators to continue exploring medical students' attitude changes towards CAM and we provide examples of what further research is needed.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".