CAM in Canadian Hospitals: The New Frontier?
Bibliographic record
Abstract
The provision in hospitals of traditional, complementary and alternative medicine (TCAM), as recognized by the World Health Organization, is now widespread in many of the world's healthcare systems. As a significant part of integrative medicine (IM) or healthcare (IHC), research has now begun to focus on the varied parameters of hospital-based TCAM, however, little research has been conducted on the topic in the Canadian context. Drawing on a multi-site case study of four Canadian hospitals, qualitative observation was conducted at hospital sites, and interviews were conducted with senior hospital leaders and biomedical and TCAM hospital practitioners. The main focus of inquiry was to obtain the views of hospital leaders on the topic of incorporating TCAM, and to examine the motivations for TCAM inclusion, economic dimensions and level of integration between TCAM and biomedicine. Hospital leaders were both highly critical of TCAM and cautiously supportive. Inclusion of TCAM was directly related to hospital leadership and institutional relationships, while TCAM practitioners remained marginalized due to economic, geographical, political and epistemological barriers. Although signs of integration were apparent, significant challenges remained that prevented TCAM practitioners from operating as fully-fledged hospital providers. An integrated change strategy is needed that engages the TCAM professions in mainstream interprofessional education and training opportunities, and that also addresses wider structural and political barriers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".