Supporting Adolescents and Young Adults through Digitally Mediated Type 1 Diabetes Transition Care: A Qualitative Descriptive Study
Bibliographic record
Abstract
Objective. The time during which adolescents and young adults (AYAs) living with Type 1 Diabetes (T1D) transition from pediatric to adult care is associated with blood sugar levels outside of target ranges, care gaps, and an increased risk of acute diabetes complications. The aim of this study was to understand (1) the perspectives of AYAs and providers about the strengths, challenges, and opportunities of transition care and (2) the role of digital technologies in supporting the transition to adult care. Research Design and Methods. We conducted a qualitative descriptive study that involved 43 semistructured interviews in French or English with AYA living with T1D (aged 16–25; n = 22) and pediatric or adult diabetes health care providers (HCPs) (n = 21). Results. We identified three themes. First, transition care is not standardized and varies widely, and there is a lack of awareness of transition guidelines. Second, virtual care can simultaneously hinder and help relationship‐building between providers and AYA. Third, AYAs value a holistic approach to care; both HCPs and AYA highlighted the opportunity to better support overall mental wellbeing. Conclusions. The design of digital technologies to support T1D transition care should consider methods for standardizing holistic care delivery and integrating hybrid diabetes care visits to support access to transition care. These findings can inform future transition intervention development that leverages existing transition guidelines, targets holistic care model integration, and considers quantitative diabetes metrics in conjunction with broader life experiences of AYA when providing transition care.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".