Canadian Naturopathic Doctor Engagement, Preparedness, and Perceptions of Evidence-Based Practice: A National Cross-Sectional Study
Bibliographic record
Abstract
Background: Despite the reported benefits of evidence-based practice (EBP), there are concerns that some practitioners, including naturopathic doctors (NDs), may be cautious about its use. The objective of this study was to explore Canadian ND perceptions, preparedness, and engagement in EBP, and the barriers and enablers to EBP uptake. Methods: The study was a national cross-sectional survey. NDs practicing in Canada were invited to complete the validated 84-item Evidence-Based Practice Attitudes and Utilization Survey between February and May 2020. Results: A total of 252 Canadian NDs were recruited. Participant attitudes toward EBP were predominantly positive, with three-quarters of participants indicating that >50% of their practice was informed by clinical research evidence. One-half of participants self-reported a medium-high to high level of skill across most EBP-related activities. Notable barriers to EBP uptake were lack of clinical evidence in naturopathy, and lack of time. Access to the internet and online databases were identified as useful enablers to improving EBP engagement. Conclusions: By shedding light on Canadian ND engagement with, preparedness for, and perceptions of EBP, the findings will help guide the development of strategies to support EBP uptake in NDs with the expectation of improving quality of care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".