Strategies for rehabilitation professionals to move evidence-based knowledge into practice: A systematic review
Bibliographic record
Abstract
RATIONALE: Rehabilitation clinicians need to stay current regarding best practices, especially since adherence to clinical guidelines can significantly improve patient outcomes. However, little is known about the benefits of knowledge translation interventions for these professionals. OBJECTIVES: To examine the effectiveness of single or multi-component knowledge translation interventions for improving knowledge, attitudes, and practice behaviors of rehabilitation clinicians. METHODS: Systematic review of 7 databases conducted to identify studies evaluating knowledge translation interventions specific to occupational therapists and physical therapists. RESULTS: 12 studies met the eligibility criteria. For physical therapists, participation in an active multi-component knowledge translation intervention resulted in improved evidence-based knowledge and practice behaviors compared with passive dissemination strategies. These gains did not translate into change in clinicians' attitudes towards best practices. For occupational therapists, no studies have examined the use of multi-component interventions; studies of single interventions suggest limited evidence of effectiveness for all outcomes measured. CONCLUSION: While this review suggests the use of active, multi-component knowledge translation interventions to enhance knowledge and practice behaviors of physical therapists, additional research is needed to understand the impact of these strategies on occupational therapists. Serious research gaps remain regarding which knowledge translation strategies impact positively on patient 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 | 0.053 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".