Technology-Enabled Collaborative Care for Diabetes and Mental Health (TECC-DM): Establishing a treatment care pathway in primary care settings
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".