The regulation of complementary and alternative medicine professions in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: This paper explains the regulation of complementary and alternative medicine (CAM) health professions, through the comparison of four distinct examples in Ontario, Canada including: chiropractors, naturopaths, homeopaths, and traditional Chinese medicine (TCM) practitioners. METHODS: This study analyzes the agenda setting and formulation stage of the policy process. In other words, it explores what happened between stakeholders before each of these CAM professions achieved regulation. Alford's model of dominant, challenging and repressed structured interests (DSIs, CSIs, and RSIs respectively) is used to describe the competition between various players within the healthcare system and their position in the health policy process. RESULTS: All four CAM professions have existed as a RSI at some point in their history, however, over the last century has sought to align themselves with various (or even become) challenging structural interests (CSIs) in order to be recognized as a regulated health profession. Dominant structural interests (DSIs), particularly the medical profession, initially largely ignored these professions' practices, unless sufficient public support of CAM practitioners' therapies warranted them to consider the need to regulate them. CONCLUSION: Unregulated CAM professions may increase their likelihood of becoming regulated if they: (1) gain popularity/strong support from patients or the general public, (2) organize themselves sufficiently that they pose a direct threat to one or more scopes of practice desirable by the DSIs and/or (3) are willing to adopt standards in education, training, and ethics that may [initially] reduce their scope of practice or profession's membership or slow their profession's growth.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".