Developing and Evaluating an Interactive, Case-Based, Web-Based Active Learning Tool for Primary Care Physicians (Community Fracture Capture Learning Hub): Protocol for an Acceptability and Engagement Study
Notice bibliographique
Résumé
BACKGROUND: The lack of osteoporosis treatment initiation after fragility fractures is a significant gap, especially in primary care. It is unclear whether barriers for primary care physicians (PCPs) arise from uncertainty about investigations, treatment initiation, or medication side effects. Key questions remain about whether active learning platforms improve treatment initiation rates better than passive methods and how PCP demographics affect learning outcomes. With PCPs increasingly using web-based platforms for continuing professional development due to time constraints and heavy workloads, an interactive community fracture capture (CFC) tool may serve as an effective alternative to in-person learning. Our CFC pilot study tested this new program's design and content, showing promising potential. OBJECTIVE: We aim to evaluate the interactive, case-based, web-based CFC Learning Hub, examining user acceptance and engagement with the platform, focusing on participants' interactions, satisfaction levels, and overall experience. METHODS: Participating PCPs are recruited through Praxhub, a web-based medical education platform, and provide electronic consent for data use after deidentification. They have been allocated into small groups (12-20 members) and join the CFC Learning Hub, a secure web-based community. This hub includes a web-based discussion forum with participant-contributed case studies and a knowledge repository. Over the 6-week program, participants will receive weekly modules with instructions, resources, discussion threads, and quizzes, along with interactive discussions moderated by experienced PCPs and physicians. The platform also hosts web-based surveys that, in combination with platform analytics, allow assessment of baseline knowledge gaps, level of activity or engagement, and improvements following the course completion. This study protocol demonstrates the creation and proposed evaluation of the CFC Learning Hub, featuring an interactive, case-based, small-group web-based learning platform equipped with flexibly scheduled, tailored modules to address the fracture treatment gap within the community. Both qualitative (via thematic analysis) and quantitative (by using 2-tailed paired t tests, Wilcoxon signed rank tests, and multivariable regression analysis) analyses will be used to assess levels of engagement and acceptance and changes in PCPs' knowledge and confidence after engagement with the CFC Learning Hub. RESULTS: Recruitment of participants started in May 2022. Data collection, analysis, and reporting will be completed following the completion of four 6-week cycles of the program. CONCLUSIONS: The study described in this protocol will provide important insights into the function and effectiveness of the CFC Learning Hub. This information will guide the expansion of the program. This initiative offers a simple digital solution for promoting current bone health practices tailored to PCPs' needs and thereafter to expand the rollout of the e-learning hub and implementation of fracture liaison models at a primary care level in Australia and elsewhere. Future applications may extend to other clinical areas and professions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57511.
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 | 0,079 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,008 |
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 ».