Examining the associations among clinician demographics, the factors involved in the implementation of evidence-based practice, and the access of clinicians to sources of information
Bibliographic record
Abstract
Background: An important way of improving healthcare services is through the implementation of evidence-based practice; but this requires an understanding of the extent to which it is occurring and the factors that are driving its implementation. Objective: To examine the associations among the demographics of clinicians, the factors involved in the implementation of evidence-based practice, and the access of clinicians to various sources of information. Study Design: Cross-sectional survey. Methods: An online survey that was distributed to 300 Canadian prosthetic and orthotic clinicians. Associations of selected survey items were determined. Results: Four primary associations were found and a further 18 were considered to be indicative of potential trends. Two of the primary associations were related to authorship and the utilization of scientific literature. Specifically, those clinicians who had previously authored or co-authored a peer-reviewed journal article were more likely to utilize scientific literature to guide their clinical practice. Conclusions: This study has highlighted important demographics which can be targeted for greater implementation of evidence-based practice. Above all, facilitating engagement of clinicians in research and its dissemination may promote a higher consumption of research evidence leading to improved evidence-based practice. Clinical relevance This study provides information about the underlying facilitators and inhibitors of evidence-based practice in prosthetics and orthotics. The findings aim to inform those involved in improving existing clinical practices, including educators, professional organizations and governing bodies.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".