Evidence-based practice, research utilization, and knowledge translation in chiropractic: a scoping review
Bibliographic record
Abstract
BACKGROUND: Evidence-based practice (EBP) gaps are widespread across health disciplines. Understanding factors supporting the uptake of evidence can inform the design of strategies to narrow these EBP gaps. Although research utilization (RU) and the factors associated with EBP have been reported in several health disciplines, to date this area has not been reviewed comprehensively in the chiropractic profession. The purpose of this review was to report on the current state of knowledge on EBP, RU, and knowledge translation (KT) in chiropractic. METHODS: A scoping review using the Arksey and O'Malley framework was used to systematically select and summarize existing literature. Searches were conducted using a combination of keywords and MeSH terms from the earliest date available in each database to May 2015. Quantitative and thematic analyses of the selected literature were conducted. RESULTS: Nearly 85 % (56/67) of the included studies were conducted in Canada, USA, UK or Australia. Thematic analysis for the three categories (EBP, RU, KT) revealed two themes related to EBP (attitudes and beliefs of chiropractors; implementation of EBP), three related to RU (guideline adherence; frequency and sources of information accessed; and perceived value of websites and search engines), and three related to KT (knowledge practice gaps; barriers and facilitators to knowledge use; and selection, tailoring, and implementation of interventions). EBP gaps were noted in the areas of assessment of activity limitation, determination of psychosocial factors influencing pain, general health indicators, establishing a prognosis, and exercise prescription. While most practitioners believed EBP and research to be important and a few studies suggested that traditional and online educational strategies could improve patient care, use of EBP and guideline adherence varied widely. CONCLUSION: Findings suggest that the majority of chiropractors hold favourable attitudes and beliefs toward EBP. However, much remains to be done for chiropractors to routinely apply evidence into clinical practice. Educational strategies aimed at practicing chiropractors can lead to more EBP and improved patient care. The chiropractic profession requires more robust dissemination and implementation research to improve guideline adherence and patient health outcomes.
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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 | 0.082 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.045 | 0.045 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".