Training Intervention and Program of Support for Fostering the Adoption of Family-Centered Telehealth in Pediatric Rehabilitation: Protocol for a Multimethod, Prospective, Hybrid Type 3 Implementation-Effectiveness Study
Bibliographic record
Abstract
BACKGROUND: Children with disability face long wait times for rehabilitation services. Before the COVID-19 pandemic, telehealth adoption was low across pediatric rehabilitation. Owing to the COVID-19 pandemic restrictions, pediatric therapists were asked to rapidly shift to telehealth, often with minimal training. To facilitate the behavior changes necessary for telehealth adoption, provision of appropriate evidence-based training and support is required. However, evidence to support the effective implementation of such training is lacking. The successful real-world implementation of a training intervention and program of support (TIPS) targeting pediatric therapists to enhance the adoption of family-centered telerehabilitation (FCT) requires the evaluation of both implementation and effectiveness. OBJECTIVE: This study aimed to evaluate TIPS implementation in different pediatric rehabilitation settings and assess TIPS effectiveness, as it relates to therapists' adoption, service wait times, families' perception of service quality, and costs. METHODS: This 4-year, pan-Canadian study involves managers, pediatric occupational therapists, physiotherapists, speech-language pathologists, and families from 20 sites in 8 provincial jurisdictions. It will use a multimethod, prospective, hybrid type 3 implementation-effectiveness design. An interrupted time series will assess TIPS implementation. TIPS will comprise a 1-month training intervention with self-paced learning modules and a webinar, followed by an 11-month support program, including monthly site meetings and access to a virtual community of practice. Longitudinal mixed modeling will be used to analyze indicators of therapists' adoption of and fidelity to FCT collected at 10 time points. To identify barriers and facilitators to adoption and fidelity, qualitative data will be collected during implementation and analyzed using a deductive-inductive thematic approach. To evaluate effectiveness, a quasi-experimental pretest-posttest design will use questionnaires to evaluate TIPS effectiveness at service, therapist, and family levels. Generalized linear mixed effects models will be used in data analysis. Manager, therapist, and family interviews will be conducted after implementation and analyzed using reflective thematic analysis. Finally, cost data will be gathered to calculate public system and societal costs. RESULTS: Ethics approval has been obtained from 2 jurisdictions (February 2022 and July 2022); approval is pending in the others. In total, 20 sites have been recruited, and data collection is anticipated to start in September 2022 and is projected to be completed by September 2024. Data analysis will occur concurrently with data collection, with results disseminated throughout the study period. CONCLUSIONS: This study will generate knowledge about the effectiveness of TIPS targeting pediatric therapists to enhance FCT adoption in pediatric rehabilitation settings, identify facilitators for and barriers to adoption, and document the impact of telehealth adoption on therapists, services, and families. The study knowledge gained will refine the training intervention, enhance intervention uptake, and support the integration of telehealth as a consistent pediatric rehabilitation service option for families of children with disabilities. TRIAL REGISTRATION: ClinicalTrials.gov NCT05312827; https://clinicaltrials.gov/ct2/show/NCT05312827. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40218.
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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 | 0.029 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".