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)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,042 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».