Technology-Enabled Collaborative Care for Diabetes and Mental Health (TECC-DM): Establishing a treatment care pathway in primary care settings
Notice bibliographique
Résumé
Background: For those living with type 2 diabetes (T2D), mental health issues including distress, anxiety, and depression are common. However, existing models of care require those living with these co-occurring conditions to navigate a fragmented healthcare system across providers, settings, and even sectors to receive adequate physical and mental health services. In a completed co-designed mixed methods explanatory sequential feasibility trial, titled Technology-Enabled Collaborative Care for Diabetes and Mental Health (TECC-DM), existing assets, including widely available technology, were leveraged to integrate T2D and mental health support through weekly virtual health coaching sessions, supported by an interdisciplinary virtual care team over 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. 31 adults with T2D and self-identified mental health challenges completed the trial with study findings revealing that the TECC-DM model is feasible and scalable, and that it additionally empowers individuals to take an active role in improving their physical and mental health. Findings also identified that while clinical and professional integration were acceptable and impactful, there was a need to better facilitate access to and treatment through primary care. This includes a need to identify and describe existing practice gaps contributing to barriers to uptake and engagement with personalized T2D self-management care in primary care settings. Objective: With the rapid shift to virtual models of care delivery, there was a need to uncover whom the TECC-DM model best supports, how to identify individuals who may benefit from the program, and how this model could be tailored, linked to, and delivered in primary care settings. Through the mobilization of the TECC-DM feasibility findings, this project served to disseminate (share findings from the co-designed program) and plan (development of an access to treatment pathway to support a future trial; future relationship and capacity building). Methods: To better understand TECC-DM study findings, a mixed methods survey of primary care providers (PCPs) was completed. Distributed through the Smoking Treatment for Ontario Patients (STOP) Program, PCPs included primary care physicians, nurse practitioners, and other allied health professionals from solo practices, family health teams, and community health centres. Partnership: In addition to the TECC-DM study team, a person with lived experience was engaged as a co-researcher in all aspects of this study. This includes development of the survey, analysis and interpretation of findings, and knowledge mobilization. Findings and Next Steps: In conjunction with TECC-DM feasibility findings, survey findings identify that using existing technology and health human resources is an acceptable solution to participants, providers, and partners. Understanding the ways by which individuals with T2D and mental health challenges access (or fail to access) treatment, including barriers to integrated care, is necessary to achieve optimal, whole person care. Leveraging findings from the TECC-DM feasibility trial and these survey findings, our team will further develop the TECC-DM model for full-scale testing.
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 | 0,016 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 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 ».