526-P: Increasing the Confidence of Health Care Professionals Treating Patients Living with Type 1 Diabetes through the Support-Pro Online Education Platform
Bibliographic record
Abstract
Introduction: Support-Pro is a comprehensive online platform that was developed for healthcare professionals (HCP) and covers all technological, treatment and lifestyle needs for type 1 diabetes (T1D) management in short, practical modules. It was developed as studies have demonstrated that HCPs who work in healthcare not specific to T1D, find it difficult to keep up-to-date with new developments that could improve T1D care and management. However, specialized online resources, such as Support-Pro, could help rectify this problem. The objective of the study is to assess whether Support-Pro can increase HCPs confidence in treating and supporting people with T1D (PWT1D) and their satisfaction with the platform. Methods: Open-label, non-randomized, 3-month trial with 142 HCPs who had access to Support-Pro, and 66 who completed 3 months on the platform (NCT04859205). Results: On average, the 66 participants (years of practice: 14±10; 90% women; 71% Caucasian; 44% dietitians, 30% nurses, 23% pharmacists, 3% physician or resident physician) reported at 3 months, an average 10% improvement in their confidence (total score 58% to 68%; p<0.0001) and a median satisfaction score of 76%. HCPs most valued features that supported knowledge gain, such as downloadable PDF documents (72%) and case studies (44%). The platform was most used in first month of access (page views 1st month: 50 [5;135] vs. 3rd month: 0 [0;18]). Conclusions: HCPs who completed 3 months on Support-Pro platform reported increased confidence in treating PWT1D and high appreciation of learning content and modalities. This platform could improve the support and advice provided to PWT1D followed-up outside specialized clinics. Disclosure A.Katz: None. R.Rabasa-lhoret: Consultant; Dexcom, Inc., Abbott, Janssen Pharmaceuticals, Inc., Novo Nordisk Canada Inc., Sanofi, Lilly, Tandem Diabetes Care, Inc., Insulet Corporation. M.K.Talbo: None. A.Housni: None. L.Hill: None. A.Roy-fleming: None. S.Haag: Other Relationship; Omnipod. A.Brazeau: Other Relationship; Dexcom, Inc., Diabète québec, Ordre des diététistes nutritionnistes du Québec, Research Support; Canadian Institutes of Health Research, Fonds de recherche du Québec en Santé. Funding Strategy Patient-Oriented 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".