What Accounts for the Appeal of Complementary/Alternative Medicine, and What Makes Complementary/Alternative Medicine "Alternative"?
Bibliographic record
Abstract
The goal of this study was to elucidate the basis for the appeal of complementary/alternative medicine (CAM) and the basis upon which people distinguish between CAM and conventional medicine. Undergraduates (N = 173) rated 19 approaches to the treatment of chronic back pain on 16 rating scales. Data were analyzed via 3-mode factor analysis, which extracted conceptual dimensions common to both the scales and the treatments. A 5-factor solution was judged togive the best description of the raters'perceptions. One of these 5 factors clearly reflected the distinction between conventional versus CAM approaches, and a 2nd factor clearly referred to treatment appeal. The other 3 factors were invasiveness, health care professional versus patient effort, and "druglikeness." To the extent that treatment was seen as a CAM treatment (as opposed to a conventional treatment), it was seen to be more appealing, less invasive, and less druglike. Simple and partial correlations of the dimension weights indicated that both the appeal of CAM and the distinction between CAM and conventional medicine were largely driven by the view that CAM is less invasive than conventional medicine.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".