Enhancing evidence-based chiropractic practice: bridging the knowledge-to-action gap for the needs of community-based chiropractors.
Bibliographic record
Abstract
Objective: To summarize key factors of knowledge translation (KT) and offer actionable recommendations to improve uptake and application of evidence-based practice (EBP) in chiropractic care. Methods: We conducted a narrative review searching for KT literature in PubMed, Web of Science, and Scopus from January 2016 to August 2024. Titles and abstracts were screened for eligibility and relevant articles underwent full-text review. We used an expert consensus approach to form our recommendations. Results: We identified KT barriers and facilitators at individual, collegial, and organizational levels. Recommendations include advocating for individual clinicians to pursue continuous education and mentorship, and for professional organizations to support KT funding and foster supportive and collaborative environments for individual clinicians to engage in KT. Conclusions: To bridge the knowledge-to-action (KTA) gap in the chiropractic profession, chiropractors should engage in learning environments to develop necessary EBP skills, while associations should focus on supporting and incentivizing chiropractors to enhance their KT abilities. Author’s Note: This paper is one of seven in a series exploring contemporary perspectives on the application of the evidence-based framework in chiropractic care. The Evidence Based Chiropractic Care (EBCC) initiative aims to support chiropractors in their delivery of optimal patient-centred care. We encourage readers to review all papers in the series.
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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 | 0.109 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".