Evaluation of <i>Support</i> , a self-guided online type 1 diabetes self-management education and support web application—a mixed methods study
Bibliographic record
Abstract
Background Type 1 diabetes requires making numerous daily decisions to maintain normoglycemia. Support is an evidence-based self-guided web application for type 1 diabetes diabetes self-management. Objective Evaluate users’ satisfaction with Support and investigate changes in self-reported frequency of-, fear of- hypoglycemia, and diabetes-related self-efficacy. Methods Adults from a Quebec type 1 diabetes registry used Support. Data was collected through online surveys or extracted from the registry at 0, 6, and 12 months (number of episodes and fear of hypoglycemia). At 6 months, participants reported satisfaction with Support and diabetes-related self-efficacy. A sub-group of 16 users was interviewed about their experience . Transcripts were analyzed using inductive and deductive approaches. Results In total, 207 accounts were created (35% men, 96% White, mean age and diabetes duration: 49.3 ± 13.8 and 25.2 ± 14.7 years). At 6 months, the median [Q1; Q3] satisfaction was 40/49 [35; 45] with a mean decrease in hypoglycemia frequency of 0.43 episodes over 3 days (95% CI: −0.86; 0.00, p = 0.051) and of −1.98 score for fear (95% CI: −3.76; −0.20, p = 0.030). Half of the participants reported increased diabetes-related self-efficacy. Conclusions Participants reported a high level of satisfaction with Support. Its use has the potential to facilitate hypoglycemia management and increase diabetes-related self-efficacy. Trial registration This study is registered on ClinicalTrials.gov NCT04233138.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".