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Record W103549301

Exploring the roles of librarians and health care professionals involved with complementary and alternative medicine.

2006· article· en· W103549301 on OpenAlexaboutno aff
Ellen Crumley

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHealth professionalsMedical educationHealth careAlternative medicineQualitative researchIntegrative medicineContinuing educationMedicinePublic relationsSociologyPolitical scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.168
GPT teacher head0.320
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2006
Admission routes1
Has abstractyes

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