Alternative, Complementary, and Conventional Medicine: Is Integration Upon Us?
Bibliographic record
Abstract
In attempts to improve their health and/or combat illness, approximately 4 in 10 Americans will use a complementary and alternative medicine (CAM) therapy this year. CAM therapies vary widely, with acupuncture, chiropractic, herbal medicine, and homeopathy among the more prominent modalities. CAM therapies are used in addition to and/or instead of the more conventional forms of medical care available in U.S. hospitals or licensed physicians' offices. A rapidly increasing interest in CAM has led to a nascent movement aimed at integrating various CAM therapies with the conventional health care system. In Washington State, for example, health insurance coverage for CAM therapies has been mandated, and a number of "integrated" delivery systems have been born. Although the political and economic forces leading to adoption and integration of CAM therapies vary widely by geographic locale, it is likely that some degree of integration will occur throughout much of the United States. Similar processes are occurring in Canada, Europe, and Australia, and indeed within middle and upper level socioeconomic strata worldwide. This paper identifies potential barriers and facilitators to potential integration, of medical disciplines and argues for an accessible, multidisciplinary and evidence-based, yet humanistic and patient-oriented approach.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".