451-P: Participants’ Satisfaction with the Support Online Self-Guided Educational Platform for Type 1 Diabetes Management: A Proof-of-Concept Study, Preliminary Results
Bibliographic record
Abstract
Introduction: The development of the SUPPORT self-guided online educational platform was guided by the behavioral wheel framework and built by healthcare professionals specialized in type 1 diabetes (T1D) and patient-partners. It has 4 learning paths that are based on the mode of insulin delivering and blood glucose monitoring of the patient. The learning modules are divided into 6 groups (e.g., medication, nutrition) and have 3 levels of complexity. Features (e.g., quiz, glossary) are included. This proof-of-concept study aims to evaluate the satisfaction of adults with T1D using the SUPPORT platform after 6 months. Methods: Adults (≥18 years old) living with T1D for ≥ 1 year, using ≥ 4 insulin injections per day or an insulin pump, and having access to the Internet were recruited from the BETTER registry in Quebec. People with ongoing pregnancy or illness limiting diabetes care were excluded. Usage data were collected with Google Analytics. A face-validated 10-item questionnaire was sent after 6 months of access to SUPPORT to evaluate participants’ satisfaction and feedback (e.g., top 3 preferred features). Results: A total of 61 participants (38% men; age 50.3±13.3 years; T1D duration 24.3±14.6 years; mean viewing time per connected session: 12min56sec) completed the 6-month questionnaire. The mean satisfaction score was 39.6±7.8 (out of 49; higher score for a greater satisfaction). The most preferred features were access to downloadable PDF summaries, quizzes, and blogs on scientific updates. Providing certificates, virtual points, and glossary were the least appreciated. More than half reported being more confident to prevent (53%) and manage (53%) hypoglycemia. Conclusion: The SUPPORT platform received a high level of satisfaction. Although further validation is needed, this platform may be a useful resource for widely and timely diabetes education and for reducing the hypoglycemia burden faced by patients. Disclosure L. Xie: None. C. Leroux: None. A. Roy-fleming: None. R. Rabasa-lhoret: Advisory Panel; Self; Bayer Inc., Covance Inc., Insulet Corporation, Pfizer Inc., Other Relationship; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Dexcom, Inc., Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Novo Nordisk, Sanofi-Aventis, Research Support; Self; Canadian Institutes of Health Research, Cystic Fibrosis Canada, Diabetes Canada, JDRF, National Institutes of Health, Prometic, Société Francophone du Diabète, Speaker’s Bureau; Self; CMS Canadian Medical&Surgical Knowledge Translation Research Group, CPD Network. A. Brazeau: None. Funding Canadian Institutes of Health Research (JT1-157204); JDRF (4-SRA-2018-651-Q-R)
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".