Development of the <i>Support</i> self-guided, web application for adults living with type 1 diabetes in Canada by a multi-disciplinary team using a people-oriented approach based on the Behaviour Change Wheel
Bibliographic record
Abstract
Background: Diabetes self-management education and support (DSME/S) are central in type 1 diabetes (T1D) where individuals are responsible for 95% of care. In-person DSME/S programs have been proven clinically effective (e.g. optimizing glycemic management, improving diabetes-related behaviors) but are limited by a lack of accessibility and long-term follow-up. Self-guided digital tools such as web applications (web apps) can be an alternative for delivering DSME/S. Objective: , a behavioral theory-based, self-guided, web application for adults living with T1D in the province of Quebec, Canada. Methods: . Patient partners first proposed its focus, learning topics, and expressed barriers to using digital tools for DSME/S. These barriers were analyzed based on the Behaviour Change Wheel. A group of healthcare professionals (HCPs) drafted the evidence-based learning content which was reviewed by external HCPs and by patient partners. Results: is a bilingual (English and French) web app accessible at any time via the Internet. It has four learning paths focusing on hypoglycemia and based on the user's method of diabetes treatment. Learning modules are divided into six categories with a maximum of three learning levels. It contains features such as a discussion forum, videos, and quizzes to ensure interactivity, provide social support, and maintain the motivation and long-term engagement of users. Conclusions: is the first self-guided evidence-based web app for adults living with T1D. It is currently under study to evaluate its feasibility and clinical impacts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".