Knowledge translation strategies used to promote evidence-based interventions for children with cerebral palsy: a scoping review
Bibliographic record
Abstract
BACKGROUND: Cerebral palsy (CP) is the most common childhood physical disability, imposing substantial costs on individuals and society. Early interventions that promote brain optimization and reorganization are vital for children with CP. Integrating early evidence-based practice (EBP) remains challenging but enhances functional outcomes. METHODS: Following a scoping review methodology, databases were searched to identify studies examining the impact of knowledge translation (KT) strategies for pediatric CP interventions. Extraction included study characteristics, methodology, KT strategies, barriers, and facilitators. Numerical and inductive content analysis identified themes among KT strategies. A final stakeholder consultation to discuss the results was conducted. RESULTS: This review included seventeen articles. Common outcomes included participant change in EBP knowledge and behaviour. Common barriers included a need for more resources, protected time, and funding. Most studies followed a multifaceted KT approach. Various KT strategies were used, primarily mentoring, workshops, case studies, and online tools. INTERPRETATION: Results underscored the need for tailored KT strategies for implementing EBP for children with CP. Additionally, user-friendly KT tools and involving mentors to facilitate the intervention can haste EBP uptake. Successful adoption depends on challenges in healthcare settings. This study provides insights into current KT strategies for advancing best practices for children with CP.
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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 | 0.028 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".