The Medical Library Association guide to finding out about complementary and alternative medicine : the best print and electronic resources
Bibliographic record
Abstract
Homeopathy, naturopathy, chiropractic and herbal medicine, massage, yoga, acupuncture, meditation, and more...complementary and alternative medicine (CAM) is now the fastest-growing sector of American health care. Librarians must be prepared to answer questions about CAM and to include authoritative, readable sources of information about it in their collections. Here's a comprehensive introduction to the major types of CAM, from Ayurveda to spiritual healing, and an up-to-date guide to 605 books, 162 websites, and 226 periodicals covering these areas. Author Gregory A. Crawford, who holds a Doctor of Naturopathy degree in addition to both an MLS and a PhD, is ideally suited to familiarize librarians with CAM. Covering both mainstream and lesser-known treatments and therapies, he provides the history and background of each topic, explaining its major uses and the training of practitioners. An extensive annotated list of books, periodicals, and websites devoted to the specific therapy includes English-language materials from the United States, Canada, the UK, and Australia. With this guide, librarians will know how to answer questions about CAM and be able to point users to the best and most reliable sources for further information. For those seeking to better represent CAM in their own libraries, the book will also prove invaluable as a collection development tool.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.194 | 0.284 |
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".