A qualitative study exploring the expectations of people living with type 1 diabetes regarding prospective use of a hybrid closed‐loop system
Bibliographic record
Abstract
AIM: To identify the expectations of a diversified sample of informed adults with type 1 diabetes on their prospective use of a hybrid closed-loop system. METHODS: Semi-structured interviews were conducted with 16 adults with type 1 diabetes who shared their expectations on an experimental hybrid closed-loop system after receiving information on its design, functioning and capability. The sample had equal representation of genders and diabetes management methods and was diversified according to age, education and occupation when possible. Qualitative content analysis of the interview transcripts with MaxQDA was used to identify expected benefits, expected inconveniences and concerns, expected improvements to design and functionalities, and interest and trust in the system. RESULTS: Participants expected benefits regarding diabetes management, clinical outcomes, psychosocial aspects of their lives, nutrition and meals, and physical activity. Participants expected inconveniences or shared concerns regarding wearability, costs and technical limitations. According to participants, improvements could be made to the system's physical appearance, practical convenience, functionalities, and software integration. Overall, 12 participants would use the system. While participants' trust could be immediate or grow over time, it could ultimately be conditional on the system's performance. CONCLUSION: Prospective users' general enthusiasm and trust foster the clinical and commercial success of hybrid closed-loop systems. However, poor user satisfaction caused by unrealistic expectations and plausible inconveniences and concerns may limit this success. Providing prospective users with comprehensive information while validating their understanding could mitigate unrealistic expectations. Improvements to design and coverage policies could favour uptake.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".