Attitudinal Changes of Health Sciences Students Participating in an Interprofessional Education (IPE) Course Enriched with Complementary and Alternative Medicine (CAM) Education and CAM Fair – A Two Year Study
Bibliographic record
Abstract
Objectives: Whole person care is ideally provided by a high functioning team, knowledgeable about all the therapies a patient has available to them (conventional or complementary), who coordinates efforts to efficiently meet all of a patient’s health care needs. Therefore, health sciences students at Canadian universities require team skills, and basic knowledge about complementary and alternative medicine (CAM). This project evaluates the attitudinal changes of health sciences students participating in a teaching intervention combining interprofessional education (IPE) and complementary and alternative medicine (CAM) education.Methods: At our university, “IntD410 Interprofessional Health Team Development” is a mandatory 10-week, 30-hour IPE course for all undergraduate health sciences students including medicine, nursing, pharmacy, physical therapy, and occupational therapy. The CAM-stream was developed by adding: (i) several didactic lectures; (ii) large group learning provided by CAM providers (i.e. Qi Gong, Mindfulness Meditation); (iii) small group work solving discovery-style cases; and (iv) the CAM Fair, providing one-on-one interactions with CAM providers. The CAM-stream was offered in 2012 (N=71) and 2013(N=120). Quantitative data was collected via pre/post validated questionnaires testing student attitudes towards CAM, interprofessional learning, and collaboration. Qualitative feedback was collected via reflective assignments and course evaluations.Results: Quantitative assessment revealed that the CAM-stream’s CAM beliefs increased, as did their recognition of the need for interprofessional learning. Qualitatively, initial reflections revealed that many students did not know what each other’s professions entailed, and they had misconceptions about CAM. Final reflections revealed the students felt more comfortable evaluating CAM practices, and recognized the value in collaboration. The CAM Fair was cited as the most impactful part of the curriculum.Conclusions: IPE and CAM education are urgently needed in Canadian universities to overcome biases and misconceptions, to develop team skills, and to provide coordinated whole person care. They are effectively taught concurrently.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".