Moving forward? Complementary and alternative practitioners seeking self‐regulation
Bibliographic record
Abstract
Complementary and alternative medicine (CAM) occupations continue to struggle towards achieving professional status, especially in the form of statutory regulation. Many consider professional status a worthwhile goal for CAM occupations, yet it is a process fraught with tensions. In this paper we present in-depth interview data from the leaders of three CAM groups (naturopaths, traditional Chinese medicine practitioners acupuncturists, and homeopaths) in Ontario, Canada that demonstrate four main strategies used by these groups to professionalize. The strategies discussed are related to how the knowledge base of each group is organised and transmitted. These strategies include: improving educational standards, improving practice standards, engaging in peer-reviewed research and increasing group cohesion. At the core of these strategies is the demarcation of who is qualified to practice, and a signalling to 'outsiders', such as medicine and the government, that practitioners are qualified and legitimate. Across the three groups, the leaders referred to the inclusion of medical science as a basis for distinguishing between 'science' and 'non-science' as well as who should practice and who should not. We highlight how internal battles over the infusion of medical science into the knowledge base are part of the process for establishing legitimacy for the three CAM groups in our study. We end with a brief discussion of the implications of these internal battles over medical science knowledge for the future of CAM groups.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".