Interprofessional education in traditional and complementary medicine: a scoping review
Bibliographic record
Abstract
Interprofessional education (IPE) is a teaching method that improves collaboration and communication across health professions. There are consistent reports of poor interprofessional collaboration and communication between conventional health professionals and traditional and complementary medicine (TCM) professions. The application of IPE within courses that provide training in TCM requires close examination. This research aimed to identify the state of the art in IPE in TCM teaching. A scoping review was conducted. Thirteen databases were searched to identify citations up to March 2021. Thirty articles were selected after filtering for relevance against the inclusion criteria. The included articles were categorized into four a priori categories: Knowledge and Attitudes of students and professionals about TCM and IPE; Competencies of IPE in TCM; Teaching about TCM using IPE and Challenges and Opportunities for IPE in TCM. Nineteen of the included articles reported empirical research and primarily presented the evaluation of IPE activities within TCM courses or workshops; six studies consisted of texts with propositions and theoretical analyses; and five were case/experiential reports of IPE and TCM interventions, with or without evaluation of results. The studies report all health science students (undergraduate and graduate) exposed to IPE demonstrate a decrease in prejudice and an increase in knowledge about TCM. A sense of partnership developed through the collaborative competencies common to IPE and TCM and integrated care of patients. IPE in the context of TCM has been used for fostering integrative health care through the collaborative work of professional teams. Implementing IPE in TCM teaching requires inclusion in the curriculum, primarily undergraduate and research training.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".