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Record W3204046177 · doi:10.54434/candj.91

Evidence-Based Practice Attitudes, Skills, and Usage Among Canadian Naturopathic Doctors: A Summary of the Evidence and Directions for the Future

2021· article· en· W3204046177 on OpenAlexaffvenueabout
Monique Aucoin, Matthew Leach, Kieran Cooley

Bibliographic record

VenueCAND Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsCanadian College of Naturopathic Medicine
Fundersnot available
KeywordsNaturopathyEvidence-based practiceMedicineHealth careEvidence-based medicineFamily medicineClinical PracticePositive attitudeAlternative medicineMedical educationPsychologyNursingSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Evidence-based practice (EBP) is a framework aimed at facilitating the delivery of best practice care. Despite documented benefits, many health professionals have expressed concerns about EBP. Naturopathic medicine has been cited as being in opposition to EBP; however, this is not supported by the evidence. In a recent cross-sectional Canadian survey of naturopathic doctors, respondents self-reported a moderate to high use of EBP and use of a range of sources of evidence to guide clinical decisions. Evidence-based practice skill was reported to be moderately high, and attitudes were predominantly positive. These findings are consistent with other research undertaken on the topic which has identified a shift towards embracing EBP. Canadian naturopathic doctors have indicated a high level of interest in improving their EBP skills, and we present an upcoming opportunity for skill development.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.010
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.339
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainMethods
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

Citations1
Published2021
Admission routes3
Has abstractyes

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