A survey of Canadian regulated complementary and alternative medicine schools about research, evidence-based health care and interprofessional training, as well as continuing education
Bibliographic record
Abstract
BACKGROUND: While some effort has been made to integrate complementary and alternative medicine (CAM) information in conventional biomedical training, it is unclear whether regulated Canadian CAM schools' students are exposed to research activities and continuing education, or whether topics such as evidence-based health care and interprofessional collaboration (IPC) are covered during their training. Since these areas are valued by the biomedical training field, this may help to bridge the attitudinal and communication gaps between these different practices. The aim of this study was to describe the training offered in these areas and gather the perceptions of curriculum/program directors in regulated Canadian CAM schools. METHODS: A two-phase study consisting of an electronic survey and subsequent semi-structured telephone interviews was conducted with curriculum/program (C/P) directors in regulated Canadian CAM schools. Questions assessed the extent of the research, evidence-based health care, IPC training and continuing education, as well as the C/P directors' perceptions about the training. Descriptive statistics were used to describe the schools', curriculum's and the C/P directors' characteristics. Content analysis was conducted on the interview material. RESULTS: Twenty-eight C/P directors replied to the electronic survey and 11 participated in the interviews, representing chiropractic, naturopathy, acupuncture and massage therapy schools. Canadian regulated CAM schools offered research and evidence-based health care training as well as opportunities for collaboration with biomedical peers and continuing education to a various extent (58% to 91%). Although directors were generally satisfied with the training offered at their school, they expressed a desire for improvements. They felt future CAM providers should understand research findings and be able to rely on high quality research and to communicate with conventional care providers as well as to engage in continuing education. Limited length of the curriculum was one of the barriers to such improvements. CONCLUSIONS: These findings seem to reinforce the directors' interest and the importance of integrating these topics in order to ensure best CAM practices and improve communication between CAM and conventional providers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".