Beyond Hemoglobin A1c—Outcomes That Matter to Individuals With Type 1 Diabetes in Adopting Digital Health Interventions for Self-Management Support: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Type 1 diabetes is a demanding chronic condition that requires diligent blood glucose monitoring and timely insulin administration by patients who must integrate self-management into their daily lives. OBJECTIVE: This study aimed to better understand what outcome measures are important to individuals living with type 1 diabetes (T1D) in Ontario, Canada, to help inform the development of type 1 diabetes virtual self-management Education and support (T1ME) trial. METHODS: A qualitative approach was used, in which we conducted 6 focus groups with a total of 24 adult participants living with T1D (from age 18 to >65 years) in Ontario. Each focus group was semistructured in nature; participants were encouraged to talk openly about their experiences with T1D self-management and provide their perspectives on more focused topics such as technology and relationships with health care providers. RESULTS: An interpretive analysis helped us devise a framework for our results that centered around 6 main discussion themes: (1) adapting self-management to meet evolving needs, (2) looking "beyond A1c" toward more personalized indicators of glycemic management, (3) the benefits and challenges of adopting new T1D technology, (4) establishing trusting relationships with diabetes care providers, (5) perceived benefits of peer support, and (6) pre- and post-COVID-19 perspectives on virtual care. CONCLUSIONS: Our goal is for these findings to help facilitate the development of patient-oriented outcome measures that are in line with the unique needs and preferences of T1D patients in this new, more virtual landscape of clinical care, education, and self-management support.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".