The Educational Impact of Web-Based, Faculty-Led Continuing Medical Education Programs in Type 2 Diabetes: A Survey Study to Analyze Changes in Knowledge, Competence, and Performance of Health Care Professionals
Notice bibliographique
Résumé
BACKGROUND: The treatment landscape for type 2 diabetes (T2D) is continually evolving; therefore, ongoing education of health care professionals (HCPs) is essential. There is growing interest in measuring the impact of educational activities, such as through use of the Moore framework; however, data on the benefits of continuing medical education (CME) in the management of T2D remain limited. OBJECTIVE: This study aimed to evaluate HCP satisfaction; measure improvements in knowledge, competence, and performance following short, case-based, multidisciplinary web-based CME activities; and identify the remaining educational gaps. METHODS: Two faculty-led, CME-accredited, web-based educational activities on T2D and obesity, touchIN CONVERSATION and touch MultiDisciplinary Team, were developed and made available on a free-to-access medical education website. Each activity comprised 3 videos lasting 10 to 15 minutes, which addressed learning objectives developed based on a review of published literature and faculty feedback. Participant satisfaction (Moore level 2) was evaluated using a postactivity questionnaire. For both activities, changes in knowledge and competence (Moore levels 3 and 4) were assessed using questionnaires completed by representative HCPs before or after participation in the activities. A second set of HCPs completed a questionnaire before and after engaging in activities that assessed changes in self-reported performance (Moore level 5). RESULTS: Each activity was viewed by approximately 6000 participants within 6 months. The participants expressed high levels of satisfaction (>80%) with both activities. Statistically significant improvements from baseline in knowledge and competence were reported following participation in touchIN CONVERSATION (mean score, SD before vs after activity: 4.36, 1.40 vs 5.42, 1.37; P<.001), with the proportion of learners answering at least six of 7 questions correctly, increasing from 22% (11/50) to 60% (30/50). A nonsignificant improvement in knowledge and competence was observed following participation in touch MultiDisciplinary Team (mean score, SD 4.36, 1.24 vs 4.58, 1.07; P=.35); however, baseline knowledge and competence were relatively high, where 80% of the respondents (40/50) answered at least four of 6 questions correctly. A significant improvement in HCP self-reported performance was observed in a combined analysis of both activities (mean score, SD 2.65, 1.32 vs 3.15, 1.26; P=.03), with the proportion of learners selecting the answer representing the best clinical option for all 4 questions increasing from 32% (11/34) to 59% (20/34) after the activity. Several unmet educational needs were self-reported or identified from the analysis of incorrectly answered questions, including setting individualized glycemic targets and the potential benefits of sodium-glucose cotransporter 2 inhibitor therapies. CONCLUSIONS: Short, case-based, web-based CME activities designed for HCPs to fit their clinical schedules achieved improvements in knowledge, competence, and self-reported performance in T2D management. Ongoing educational needs identified included setting individualized glycemic targets and the potential benefits of sodium-glucose cotransporter 2 inhibitor therapies.
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 | 0,004 | 0,010 |
| 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 | 0,001 | 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) | 0,002 | 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 ».