The Growing Need to Teach about Complementary and Alternative Medicine
Bibliographic record
Abstract
With the increased popularity of complementary and alternative medicine (CAM), there is a growing interest in the topic among physicians, residents, and medical students, who feel an increased need to have proper instruction about CAM therapies. Medical schools and residency programs are starting to respond to this demand, having realized that to provide better care and foster an improved patient-doctor relationship, physicians should become informed consultants, and be able to provide educated advice about CAM to their patients and help them integrate any CAM therapies shown to be safe and effective into their health care. The authors acknowledge that opinions differ about the adequacy of research findings to certify the safety and efficacy of specific therapies, and stress that physicians' decisions about CAM use should be subject to the same exacting criteria employed by researchers to evaluate any new therapies. The authors report on CAM curriculum developments in Germany, Canada, and the United States that illustrate various approaches to the question, "What should be taught in a CAM course?" In most cases, the approach is to teach about CAM therapies, although in others, therapies that the curriculum planners considered useful and safe are being integrated into the medical curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".