Coproducing Knowledge of the Implementation of Complex Digital Health Interventions for Adults with Acquired Brain Injury and their Communication Partners: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: The Social Brain Toolkit, conceived and developed in partnership with stakeholders, is a novel suite of web-based communication interventions for people with brain injury and their communication partners. To support effective implementation, the developers of the Social Brain Toolkit have collaborated with people with brain injury, communication partners, clinicians, and individuals with digital health implementation experience to coproduce new implementation knowledge. In recognition of the equal value of experiential and academic knowledge, both types of knowledge are included in this study protocol, with input from stakeholder coauthors. OBJECTIVE: This study aims to collaborate with stakeholders to prioritize theoretically based implementation targets for the Social Brain Toolkit, understand the nature of these priorities, and develop targeted implementation strategies to address these priorities, in order to support the Social Brain Toolkit's implementation. METHODS: Theoretically underpinned by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework of digital health implementation, a maximum variation sample (N=35) of stakeholders coproduced knowledge of the implementation of the Social Brain Toolkit. People with brain injury (n=10), communication partners (n=11), and clinicians (n=5) participated in an initial web-based prioritization survey based on the NASSS framework. Survey completion was facilitated by plain English explanations and accessible captioned videos developed through 3 rounds of piloting. A speech-language pathologist also assisted stakeholders with brain injury to participate in the survey via video teleconference. Participants subsequently elaborated on their identified priorities via 7 web-based focus groups, in which researchers and stakeholders exchanged stakeholder perspectives and research evidence from a concurrent systematic review. Stakeholders were supported to engage in focus groups through the use of visual supports and plain English explanations. Additionally, individuals with experience in digital health implementation (n=9) responded to the prioritization survey questions via individual interview. The results will be deductively analyzed in relation to the NASSS framework in a coauthorship process with people with brain injury, communication partners, and clinicians. RESULTS: Ethical approval was received from the University of Technology Sydney Health and Medical Research Ethics Committee (ETH20-5466) on December 15, 2020. Data were collected from April 13 to November 18, 2021. Data analysis is currently underway, with results expected for publication in mid-2022. CONCLUSIONS: In this study, researchers supported individuals with living experience of acquired brain injury, of communicating with or clinically supporting someone post injury, and of digital health implementation, to directly access and leverage the latest implementation research evidence and theory. With this support, stakeholders were able to prioritize implementation research targets, develop targeted implementation solutions, and coauthor and publish new implementation findings. The results will be used to optimize the implementation of 3 real-world, evidence-based interventions and thus improve the outcomes of people with brain injury and their communication partners. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35080.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,083 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,005 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,064 | 0,013 |
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 ».