Teaching Evidence-Based Complementary and Alternative Medicine (EBCAM); Changing behaviours in the face of reticence: A cross-over trial
Bibliographic record
Abstract
BACKGROUND: The effectiveness of teaching critical appraisal to students of Complementary and Alternative Medicine (CAM) has not been studied. In this study we attempt to determine if a workshop for final year students at a naturopathic college improved their ability to utilize critical appraisal concepts. METHODS: We assigned 83 Naturopathic Interns to two groups: Group A (n = 47) or Group B (n = 36). We conducted a baseline assessment of all subjects' critical appraisal skills. Group A was assigned to receive a 3(1/2) hour workshop on Evidence Based Medicine (EBM) and Group B received a workshop on bioethics (control intervention). The groups critical appraisal skills were re-evaluated at this time. We then crossed over the intervention so that Group B received the EBM workshop while Group A received the bioethics workshop. Assessment of critical appraisal skills of the two groups was again performed. RESULTS: The students mean scores were similar in Group A (14.8) and Group B (15.0) after Group A had received the intervention and Group B had received the control (p = 0.75). Group scores were not significantly improved at the end of the trial compared to at the beginning of the study (Group A: 15.1 to 16.1) (Group B 15.6 to 15.9). Student's confidence in reading research papers also did not improve throughout the course of the study. CONCLUSION: The final year is a difficult but important time to teach critical appraisal and evidence skills. Single, short intervention programs will likely yield negligible results. A multi-factorial approach may be better suited to implementing EBCAM than single short interventions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".