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Record W2065392030 · doi:10.1177/1533210108325549

A Literature Review of Health Care Professional Attitudes Toward Complementary and Alternative Medicine

2008· review· en· W2065392030 on OpenAlexaffabout
Maida Sewitch, Monica Cepoiu‐Martin, Nicole Rigillo, Donald Sproule

Bibliographic record

VenueComplementary health practice review · 2008
Typereview
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsSt Mary's Hospital CentreMcGill University
Fundersnot available
KeywordsMedicineHealth careHealth professionalsFamily medicineAlternative medicineReferralMedical prescriptionNursingMEDLINEMainstream

Abstract

fetched live from OpenAlex

Objective. To summarize health care professionals' attitudes toward complementary and alternative medicine (CAM). Methods. In October 2006, we searched Allied and Complementary Medicine Database (AMED; 1985—2006), Excerpta Medica Database (EMBASE; 1980—2006), and MED-LINE (1960—2006) for Canadian or US studies of health care professionals' attitudes toward CAM, published in English or French. Results. A total of 21 surveys of physicians, nurses, public health professionals, dietitians, social workers, medical/nursing school faculty, and pharmacists were included that focused on beliefs about CAM efficacy, personal use, clinical practice use and referrals, communication with patients about CAM, level of knowledge, and the need for information regarding various CAM therapies. Physicians were more negative compared to other health care professionals. Positive attitudes toward CAM did not correlate with CAM referral or prescription patterns. Health care professionals of all disciplines wanted more information about CAM. Conclusions. Heterogeneity in the CAM definition and questionnaire items precluded summarizing health care professionals' attitudes toward CAM. Providing CAM education to health care professionals may help to integrate CAM into mainstream medical care.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.238
GPT teacher head0.556
Teacher spread0.318 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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

Citations85
Published2008
Admission routes2
Has abstractyes

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