Education strategies are the most commonly used in pediatric rehabilitation implementation research: a scoping review
Bibliographic record
Abstract
BACKGROUND: Approximately one in six children has a disability, and effective, evidence-based rehabilitation can ameliorate the impact of these conditions over the lifespan. However, implementing interventions in real-world settings remains a challenge. This scoping review aimed to summarize the characteristics, implementation strategies, and outcomes of implementation studies in pediatric rehabilitation. METHODS: A comprehensive search was conducted in PubMed/MEDLINE, EMBASE, CINAHL, SCOPUS, and Web of Science from the database inception to December 2, 2022. Studies testing implementation strategies in pediatric rehabilitation interventions were included. Data extracted included study characteristics (e.g., country, intervention type, field of rehabilitation), implementation strategies characterized using the Expert Recommendations for Implementing Change taxonomy, and outcomes based on the Implementation Outcomes Framework. RESULTS: Of the 11,740 studies identified, 44 met the inclusion criteria. Most studies were conducted in the United States (n = 15, 34%) or Canada (n = 10, 23%) and used a mixed-methods design (n = 13, 30%). Interventions primarily targeted motor skills (n = 19, 43%) and were conducted in outpatient settings (n = 14, 32%) or homes (n = 11, 23%). The most commonly used implementation strategies were "train and educate key informant" (n = 21, 48%) and "use evaluative/iterative strategies" (n = 19, 43%). Feasibility (n = 19, 43%) and acceptability (n = 16, 36%) were the most frequently targeted implementation outcomes. CONCLUSIONS: Reporting implementation strategies and outcomes in pediatric rehabilitation studies is limited and highly variable. Most strategies focused on developing and sharing educational materials, while administrative and systems-level interventions were largely absent. Standardized documentation of implementation strategies and outcomes could advance the field's understanding of the effective development of interventions designed for implementation, encouraging faster uptake of effective interventions.
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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 | 0.057 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.036 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".