A qualitative study on the term CAM: is there a need to reinvent the wheel?
Bibliographic record
Abstract
BACKGROUND: As complementary and alternative medicine (CAM) has developed extensively, uncertainty about the appropriateness of the terms CAM and other CAM-related terms has grown both in the research and practice communities. Various terms and definitions have been proposed over the last three decades, highlighting how little agreement exits in the field. Contextual use of current terms and their respective definitions needs to be discussed and addressed. METHODS: Relying upon the results of a large international Delphi survey on the adequacy of the term CAM, a focus group of 13 international experts in the field of CAM was held. A forum was also set up for 28 international experts to discuss and refine proposed definitions of both CAM and integrative healthcare (IHC) terms. Audio recordings of the meeting and forum discussion threads were analyzed using interpretive description. RESULTS: Multiple terms to describe the therapies, products, and disciplines often referred to as CAM, were considered. Even though participants generally agreed there is a lack of optimal definitions for popular CAM-related umbrella terms and that all terms that have so far been introduced are to some extent problematic, CAM and IHC remained the most popular and accepted terms by far. The names of the specific disciplines were also deemed adequate in certain contexts. Focus group participants clarified the context in which those three terms are appropriate. Existing and emergent definitions of both CAM and integrative healthcare terms were discussed. CONCLUSIONS: CAM and other related terms could be used more effectively, provided they are used in the proper context. It appears difficult for the time being to reach a consensus on the definition of the term CAM due to the uncertainty of the positioning of CAM in the contemporary healthcare systems. While umbrella terms such as CAM and IHC are useful in the context of research, policy making and education, relevant stakeholders should limit the use of those terms.
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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 | 0.043 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".