Facilitators and barriers to implementation of early intensive manual therapies for young children with cerebral palsy across Canada
Bibliographic record
Abstract
BACKGROUND: Cerebral Palsy (CP) is the most common childhood-onset motor disability. Play-based early intensive manual therapies (EIMT) is an evidence-based practice to improve long-term hand function particularly for children with asymmetric hand use due to CP. For children under two years old, this therapy is often delivered by caregivers who are coached by occupational therapists (OTs). However, why only a few Canadian sites implement this therapy is unclear. There is a need to identify strategies to support implementation of EIMT. The primary objective of this study was to identify the facilitators and barriers to EIMT implementation from the perspectives of (1) caregivers of children with CP (2), OTs and (3) healthcare administrators for paediatric therapy programs. METHODS: The Consolidated Framework for Implementation Research (CFIR) was used to guide development of an online 5-point Likert scale survey to identify facilitators (scores of 4 and 5) and barriers (scores of 1 and 2) to implementation of EIMT. Three survey versions were co-designed with knowledge user partners for distribution to caregivers, OTs, and healthcare administrators across Canada. The five most frequently endorsed facilitators and barriers were identified for each respondent group. RESULTS: Fifteen caregivers, 54 OTs, and 11 healthcare administrators from ten Canadian provinces and one territory participated in the survey. The majority of the identified facilitators and barriers were within the 'Inner Setting' CFIR domain, with 'Structural Characteristics' emerging as the most reported CFIR construct. Based on the categorization of the most frequently endorsed facilitators and barriers within the CFIR domains, the key facilitators to EIMT implementation included the characteristics of the intervention and establishing positive workplace relationships and culture. The key barriers included having workplace restrictions on EIMT delivery models and external influences (e.g., funding) on EIMT uptake. CONCLUSIONS: We identified key facilitators and barriers to implementing EIMT from a multi-level Canadian context. These findings will inform the next steps of designing evidence-informed and theory-driven implementation strategies to support increased delivery of EIMT for children under two years old with asymmetric hand use due to CP across Canada.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".