An e-Learning Program for Physiotherapists to Manage Knee Osteoarthritis Via Telehealth During the COVID-19 Pandemic: Real-World Evaluation Study Using Registration and Survey Data
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic necessitated clinicians to transition to telehealth, often with little preparation or training. The Physiotherapy Exercise and Physical Activity for Knee Osteoarthritis (PEAK) e-learning modules were developed to upskill physiotherapists in management of knee osteoarthritis (OA) via telehealth and in-person. In the research setting, the e-learning modules are perceived by physiotherapists as effective when they are part of a comprehensive training program for a clinical trial. However, the effectiveness of the modules on their own in a real-world setting is unknown. OBJECTIVE: This study aims to evaluate the reach, effectiveness, adoption, and implementation of PEAK e-learning modules. METHODS: This longitudinal study was informed by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Participants were clinicians, researchers, educators, and health care students who registered for access to the modules between April 1 and November 30, 2020. Reach was evaluated by outcomes (countries, referral sources, and attrition) extracted from registration data and embedded within precourse surveys in the Learning Management System (LMS). Effectiveness was evaluated by outcomes (confidence with videoconferencing; likelihood of using education, strengthening exercise, and physical activity in a treatment plan for knee OA; usefulness of modules) measured using a 10-point numeric rating scale (NRS; score range from 1=not confident or likely or useful at all to 10=extremely confident or likely or useful) in pre- and postcourse (on completion) surveys in the LMS. Adoption and implementation were evaluated by demographic and professional characteristics and outcomes related to the use of learning and usefulness of program elements (measured via a 4-point Likert scale, from not at all useful to extremely useful) in a survey administered 4 months after module completion. RESULTS: Broad reach was achieved, with 6720 people from 97 countries registering for access. Among registrants, there were high levels of attrition, with 36.65% (2463/6720) commencing the program and precourse survey and 19.61% (1318/6720) completing all modules and the postcourse survey. The program was effective. Learners who completed the modules demonstrated increased confidence with videoconferencing (mean change 3.1, 95% CI 3.0-3.3 NRS units) and increased likelihood of using education, strengthening and physical activity in a knee OA treatment plan, compared to precourse. Adoption and implementation of learning (n=149 respondents) occurred at 4 months. More than half of the respondents used their learning to structure in-person consultations with patients (80/142, 56.3%) and patient information booklets in their clinical practice (75/142, 52.8%). CONCLUSIONS: Findings provide evidence of the reach and effectiveness of an asynchronous self-directed e-learning program in a real-world setting among physiotherapists. The e-learning modules offer clinicians an accessible educational course to learn about best-practice knee OA management, including telehealth delivery via videoconferencing. Attrition across the e-learning program highlights the challenges of keeping learners engaged in self-directed web-based learning.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».