Technology-Enabled Collaborative Care for Diabetes and Mental Health (TECC-D): Feasibility and Satisfaction of a Co-Re-Designed Integrated Virtual Care Model
Notice bibliographique
Résumé
Objective: For individuals living with Type-2 Diabetes (T2D), mental health issues such as distress, anxiety, and depression are common. However, current models of care require those living with T2D and mental health issues to navigate a fragmented healthcare system to receive physical and mental health services. This is especially true in rural areas where there are additional barriers at the individual and provider level. To address these gaps, we co-re-designed a person-centred Technology-Enabled Collaborative Care for Diabetes and Mental Health (TECC-D) model through iterative input. Co-Re-Design: To meet the complex needs of those living with T2D and mental health issues, a co-re-design process was utilized. This involved building on existing assets, including a previously co-designed collaborative care model that was re-designed for this population with input from three people with lived experience of diabetes, diabetes and mental health care providers, and community stakeholders. This team supported the design and implementation of the model throughout the study duration. Methods: In this explanatory sequential feasibility trial, the TECC-D model leveraged existing assets including widely available technology (telephone, web-conferencing) to integrate T2D and mental health support through weekly, virtual health coaching sessions with a Certified Diabetes Educator, supported by a multidisciplinary virtual care team for 8 weeks. Primary outcomes included the feasibility and acceptability of the TECC-D model with exploratory outcomes including changes in mental health, substance use, and physical health behaviours collected at baseline, 4, 8, and 12 weeks. Provider and partner experience and satisfaction were also explored through a qualitative descriptive study. Results: 31 participants were recruited and completed the program between June 2021 and June 2022. Study findings demonstrate that the TECC-D model is a feasible and scalable care solution that empowers individuals living with T2D and mental health issues to take an active role in improving their physical and mental health. Participants and providers described program components (e.g., health coaching, virtual care team) and program delivery (e.g., frequency and duration of the program) positively. This included: 1) seeing virtual care as practical and effective for managing routine care needs; 2) feeling that having an expert care coach supported by a virtual care team was “over and above” the care that participants had previously experienced; 3) building expertise and capacity within the care team specific to diabetes and mental health; and 4) recognizing that integrated care is “missing from diabetes care” and that in offering clinical integration, participants felt “much more than just the sum of my parts”. Conclusions: Utilizing widely available technology in the current health system and engaging existing primary and community-based care assets, this model of care was found to be both feasible and acceptable by participants, providers, and patient partners and may improve the lives of individuals living with T2D and mental health issues.
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 | 0,027 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».