Evidence-based early rehabilitation for children with cerebral palsy: co-development of a multifaceted knowledge translation strategy for rehabilitation professionals
Bibliographic record
Abstract
Background: Cerebral palsy (CP) is the most common childhood physical disability. Early and evidence-based rehabilitation is essential for improving functional outcomes in children with CP. However, rehabilitation professionals face barriers to adopting evidence-based practices (EBP)s. The objective of this project is to develop a knowledge translation (KT) strategy to support CP-EBP among pediatric rehabilitation professionals. Methods: We follow an integrated KT approach by collaborating with clinician- and patient-partners. Partners engaged in co-design through team meetings and content review via email. The KT strategy comprises two components: (1) An electronic (e)-KT toolkit was created from summarized evidence extracted from randomized clinical trials on early rehabilitation for children with CP, and (2) a multifaceted online KT training program developed with guidance from a scoping review exploring effective KT strategies. Results: The e-KT toolkit summarizes twenty-two early interventions for children with or at risk for CP aged 0-5 years. Each module features an introduction, resources, parent/family section, and clinician information, including outcomes, intervention effectiveness, and evidence level. The KT training program includes three 10-15 min video-based training modules, text summaries, quizzes, and case studies. Site champions, identified as qualified rehabilitation professionals, were onboarded to support the site implementation of the training program. A champion-training booklet and 1-hour session were designed to equip them with the necessary knowledge/resources. Conclusion: The tailored, multifaceted, and co-designed KT strategy aims to be implemented in pediatric rehabilitation sites to support professional's uptake of CP-EBPs. Lessons learned from its development, including the co-development process and multifaceted nature, hold potential for broader applications in rehabilitation.
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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 | 0.151 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".