Delphi-Derived Development of a Common Core for Measuring Complementary and Alternative Medicine Prevalence
Bibliographic record
Abstract
Assessing complementary and alternative medicine (CAM) use remains difficult due to many problems, not the least of which is defining therapies and modalities that should be considered as CAM. Members of the International Society for Complementary Medicine Research (ISCMR) participated in a Delphi process to identify a core listing of common CAM therapies presently in use in Western countries. Lists of practitioner-based and self-administered CAM were constructed based on previous population-based surveys and ranked by ISCMR researchers by perceived level of importance. A total of 64 (49%) ISCMR members responded to the first round of the Delphi process, and 39 of these (61%) responded during the second round. There was agreement across all geographic regions (United States, United Kingdom, Canada, and Western Europe) for the inclusion of herbal medicine, acupuncture, homeopathy, Traditional Chinese Medicine (TCM), chiropractic, naturopathy, osteopathy, Ayurvedic medicine, and massage therapy in the core practitioner-based CAM list, and for homeopathy products, herbal supplements, TCM products, naturopathic products, and nutritional products in the self-administered list. This Delphi process, along with the existing literature, has demonstrated that (1) separate lists are required to measure practitioner-based and self-administered CAM; (2) timeframes should include both ever use and recent use; (3) researchers should measure and report prevalence estimates for each individual therapy so that direct comparisons can be made across studies, time, and populations; (4) the list of CAM therapies should include a core list and additionally those therapies appropriate to the geographic region, population, and the specific research questions addressed, and (5) intended populations and samples studied should be defined by the researcher so that the generalizability of findings can be assessed. Ultimately, it is important to find out what CAM modality people are using and if they are being helped by these interventions.
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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 | 0.157 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".