Exploring the roles of librarians and health care professionals involved with complementary and alternative medicine.
Bibliographic record
Abstract
OBJECTIVES: The researcher conducted qualitative research about the role of health care professionals and librarians involved with complementary and alternative medicine (CAM). The goals were to identify resources these professionals use to explore the librarians' role as well as their approaches to teaching and searching with respect to CAM, to acquire information about CAM education, and to connect with other librarians in the CAM field. METHODS: Semi-structured interviews with open-ended questions were used. RESULTS: Sixteen health care and information professionals from ten different institutions in Boston, Baltimore, and Calgary were interviewed. Major themes from the interviews were: CAM funding, integration of CAM and conventional medicine, roles of librarians, "hot" CAM issues, and information access. Information about four aspects of CAM education--technology, undergraduate, graduate, and continuing--is presented. A wealth of information resources was identified. CONCLUSIONS: A CAM librarian's role is unique; many specialize in specific areas of CAM, and opportunities exist for librarians to partner with CAM groups. CAM information professionals' major roles involve information access and retrieval and education. Further study is required concerning CAM consumer health, integrative CAM and conventional medicine models, and the librarian's role in a CAM environment. CAM funding is a major concern.
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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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".