Implementation of a knowledge translation strategy to promote early evidence‐based rehabilitation for children with cerebral palsy
Bibliographic record
Abstract
AIM: To understand the impact of a knowledge translation strategy that integrates the Early Detection and Intervention Toolkit for Cerebral Palsy (EDIT-CP) electronic knowledge translation toolkit to support rehabilitation professionals using evidence-based interventions. METHOD: A mixed-methods study using the Knowledge-to-Action Framework evaluated the knowledge translation strategy. Twenty-three rehabilitation professionals participated in a 15-week knowledge translation strategy, including online training, newsletters, and support for site champions. Baseline and post-intervention assessments measured changes in evidence-based practice (EBP) activities, attitudes, confidence, and resources. Feasibility, acceptability, and appropriateness of the toolkit were also assessed. Focus groups provided qualitative data on organizational barriers and facilitators. RESULTS: No significant change was found across the three EBP constructs. The largest effect was for EBP attitudes/confidence (r = 0.49), followed by resources (r = 0.26) and activities (r = 0.22). One mentorship-related item reached significance (p < 0.05). Participants rated the toolkit as acceptable and feasible, though systemic challenges may limit sustainability. INTERPRETATION: The knowledge translation strategy enhanced EBP uptake among rehabilitation professionals working with children with cerebral palsy. Addressing organizational challenges is essential for long-term success.
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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 | 0.043 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".