Training Intervention and Program of Support (TIPS) for fostering adoption of family-centred telehealth in pediatric rehabilitation: Protocol for a multi-method, prospective hybrid type 3 implementation-effectiveness study (Preprint)
Bibliographic record
Abstract
BACKGROUND Children with disability face long wait times for rehabilitation services. Prior to the pandemic, telehealth adoption was low across most rehabilitation settings, including pediatrics. As a result of the pandemic restrictions, pediatric therapists were asked to rapidly shift to using telehealth without previous experience and often minimal training. To facilitate the behaviour change necessary for telehealth adoption, provision of appropriate evidence-based training and support is required. Evidence to support effective implementation of such training is lacking. The successful real-world implementation of a training intervention and program of support (i.e., TIPS) targeting pediatric therapists to enhance adoption of family-centered telehealth (FCT) requires evaluation of both implementation and effectiveness. OBJECTIVE This study aims to: 1) evaluate TIPS implementation in different pediatric rehabilitation settings, 2) assess TIPS effectiveness, as it related to telehealth adoption on therapists, services, and families, 3) document the impact of telehealth adoption on therapists, services, and families on therapists, services, and families METHODS This 4-year, pan-Canadian study, involving managers, pediatric occupational therapists, physiotherapists, speech and language pathologists, and families from 20 sites in 8 provincial jurisdictions will use a multi-method, prospective, hybrid type 3 implementation-effectiveness design. To examine the implementation, an interrupted time series will assess TIPS implementation. TIPS will be comprised of 1) a one-month training intervention with self-paced learning modules and a webinar, followed by 2) an 11-month support program including monthly site meetings and access to a virtual community of practice. We will use longitudinal mixed modeling to analyze indicators of therapists’ adoption of FCT and fidelity to FCT collected at 9 time-points. To identify barriers and facilitators to adoption and fidelity, qualitative data will be collected during implementation and analysed using a deductive-inductive thematic approach. To evaluate effectiveness, quasi-experimental pre-, post-test design will use questionnaires to evaluate TIPS effectiveness at service, therapist, and family levels. Data will be analysed using generalised linear mixed effect models. Manager, therapist, and family interviews will also be conducted post-implementation and analyzed using reflective thematic analysis. Finally, we will gather cost data and calculate costs to the public system and to society. RESULTS We received ethics approval on February 8, 2022, in one jurisdiction; approval is pending in the others. Twenty sites have been recruited and data collection is anticipated to commence in September 2022 with completion by September 2024. Data analysis will occur concurrently with data collection, with dissemination of results expected throughout the study period. CONCLUSIONS This study will generate knowledge about the effectiveness of TIPS targeting pediatric therapists to enhance adoption of FCT in pediatric rehabilitation settings, identify facilitators and barriers to adoption, and 3) document the impact of telehealth adopti3) document the impact of telehealth adoption on therapists, services, and families on therapists, services, and families. The knowledge gained from this study will refine the training intervention, enhance intervention uptake, and support the integration of telehealth as a consistent pediatric rehabilitation service delivery option for families of children with disability. CLINICALTRIAL Clinicaltrials.gov: NCT05312827
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".