Knowledge Translation Interventions to Increase the Uptake of Evidence-Based Practice Among Pediatric Rehabilitation Professionals: A Systematic Review
Bibliographic record
Abstract
Aims To summarize evidence on effective knowledge translation (KT) interventions to increase evidence-based practice among pediatric rehabilitation professionals.Methods The PRISMA 2020 Checklist was used for search, selection, and data extraction. The Mixed Methods Appraisal Tool assessed quality, while the Cochrane Effective Practice and Organization of Care described KT interventions.Results Our search yielded 7233 records; 29 met the inclusion criteria. Two studies used educational materials alone. The remaining 27 studies used multifaceted KT interventions, combining education with local opinion leaders or knowledge brokers (n = 12), continuous quality improvement (n = 11), reminders (n = 6), communities of practice (n = 6), provider performance monitoring (n = 5), audit and feedback (n = 2), information and communication technology (n = 2), clinical practice guideline (n = 1), or routine patient-reported outcome measures (n = 1). Significant improvements in knowledge (n = 10), skills (n = 6), adherence and use (n = 4), perceptions (n = 3), intentions of use (n = 2), confidence level (n = 1), beliefs and attitudes (n = 3), and professional behavior change (n = 2) were reported. Included studies had a moderate to high-risk bias.Conclusion Educational and local opinion leaders were common in multifaceted KT interventions, the majority resulting in improved professional knowledge, skills, attitudes and beliefs, and behaviors. Rigorous study designs are needed to confirm these findings.
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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 | 0.029 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".