Impact on Knowledge, Competence, and Performance of a Faculty-Led Web-Based Educational Activity for Type 2 Diabetes and Obesity: Questionnaire Study Among Health Care Professionals and Analysis of Anonymized Patient Records
Bibliographic record
Abstract
BACKGROUND: Strategies for managing type 2 diabetes (T2D) and obesity are evolving with the introduction of targeted therapies, including incretin-based dual agonists and growing knowledge of the importance of multidisciplinary care. Accessible, effective continuing medical education (CME) activities are required to ensure that health care professionals (HCPs) understand and can implement the most recent data to optimize patient outcomes. OBJECTIVE: We aimed to measure changes in knowledge, competence, and self-reported performance and quantitatively evaluate changes in performance using anonymized patient data following participation in a web-based educational activity. The faculty-led CME-accredited activity was based on incretin-based dual agonists and patient education on T2D and obesity. The remaining educational gaps in this field were also identified. METHODS: A CME-accredited, web-based, multidisciplinary (touchMDT) educational activity titled "The future for glycemic control and weight loss in T2D and obesity: Incretin-based dual-agonists and optimizing patient education" was developed. HCP knowledge, competence, and performance were assessed before and after the activity against Moore's expanded outcomes framework (levels 1-5), using self-reported questionnaires and by analyzing anonymized patient record data. RESULTS: For evaluating knowledge and competence (50 respondents before and 50 learners after the activity), the mean number of correctly answered questions was significantly higher post activity (median 5.0, IQR 4.0-6.0 to 6.0, IQR 5.0-7.0; mean 4.98, SD 1.22 to 5.78, SD 1.13; P<.001). Modest, nonsignificant improvements in self-reported performance (N=50 respondents preactivity; N=50 learners postactivity) from before to after the activity were observed (median 4.0, IQR 3.25-4.0 to 4.0, IQR 4.0-4.0; mean 3.64, SD 0.69 to 3.76, SD 0.48; P=.32). PPatient data analysis indicated that patients were being treated more intensively postactivity: before the activity, the most commonly used treatment regimens were metformin monotherapy (13/50, 26%) and dual therapy with metformin plus injectable glucagon-like peptide-1 (GLP-1) receptor agonist (RA; 11/50, 22%); post activity, this changed to dual therapy with metformin plus injectable GLP-1 RA (12/50, 24%) and triple therapy with metformin plus injectable GLP-1 RA plus sodium-glucose cotransporter-2 inhibitor (SGLT2i; 10/50, 20%). In addition, there was an increased number of referrals to a combination of specialists (physicians referred 27%, 8/30 of patients to ≥2 specialists before the activity and 36%, 10/28 to ≥2 specialists post activity). The remaining educational gaps included understanding the biology and psychology of obesity, efficacy and safety data for incretin-based dual agonists, and the role of the diabetes educator or diabetes care and education specialist in managing T2D and obesity. CONCLUSIONS: This short, web-based CME activity on the management of T2D and obesity led to improvements in HCP knowledge, competence, and performance. Several remaining unmet needs were identified, which can be used to inform the content of future educational activities in this disease area.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".