Technology-Enabled Collaborative Care for Type-2 Diabetes and Mental Health (TECC-D): Findings From a Mixed Methods Feasibility Trial of a Responsive Co-Designed Virtual Health Coaching Intervention
Bibliographic record
Abstract
Introduction: Type-2 diabetes (T2D) is a complex chronic condition associated with a lower quality of life due to disease specific distress. While there is growing support for personalized diabetes programs, care for mental health challenges is often fragmented and limited by access to psychiatry, and integration of care. The use of communication technology to improve team based collaborative care to bridge these gaps is promising but untested. Methods: We conducted an explanatory sequential mixed methods study to assess the feasibility and acceptability of the co-designed Technology-Enabled Collaborative Care for Diabetes and Mental Health (TECC-D) program. Participants included adults aged ≥18 years who had a clinical diagnosis of T2D, and self-reported mental health concerns. Results: 31 participants completed the 8-week virtual TECC-D program. Findings indicate that the program is feasible and acceptable and indicate that there is a role for virtual diabetes and mental health care. Discussion: The TECC-D program, designed through an iterative co-design process and supported by innovative, responsive adaptations led to good uptake and satisfaction. Conclusion: The TECC-D model is a feasible and scalable care solution that empowers individuals living with T2D and mental health concerns to take an active role in their care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".