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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».