Patient-Centered Education Through A Massive Open Online Course (MOOC) for Patients With Multiple Myeloma and Caregivers: Descriptive Study of Knowledge Gains by French Association of Patients With Multiple Myeloma (AF3M) and French-Speaking Myeloma Intergroup (IFM)
Notice bibliographique
Résumé
Background: Multiple myeloma (MM) is a chronic hematologic malignancy characterized by complex therapeutic strategies, repeated relapses, and substantial information and psychosocial needs. Advances in oral therapies and outpatient management have shifted greater responsibility to patients and caregivers, emphasizing the need for accessible, high-quality educational resources. Therapeutic patient education (TPE) aims to empower patients to understand and manage their condition more effectively. Digital education tools such as massive open online courses (MOOCs) represent an innovative approach to deliver structured, interactive, and scalable learning experiences to large patient populations. However, few MOOCs have been specifically designed for patients with oncological or hematological disorders, and even fewer have been rigorously evaluated for their educational impact. Objective: This study aimed to develop and evaluate a MOOC co-designed with patients, caregivers, and health care professionals to improve knowledge, skills, and empowerment among patients living with MM and their relatives. Secondary objectives included assessing participant satisfaction, engagement, and the feasibility of this digital education model at a national scale. Methods: The MOOC "Understanding and Living with Myeloma" was jointly developed by the French Association of Patients with Multiple Myeloma (AF3M) and the French-Speaking Myeloma Intergroup (IFM). The program consisted of 5 thematic modules delivered over 8 weeks, covering disease mechanisms, diagnosis, treatment options, side-effect management, and daily-life adaptation. Content combined educational videos, self-assessment quizzes, peer-tutoring forums, and live web conferences with experts. Participants self-assessed their knowledge using a 52-item questionnaire rated from 1 (poor) to 5 (excellent) before and after completing the program. Descriptive and inferential analyses were performed using the Wilcoxon signed-rank test (2-sided α=.05). Results: During the first session, 254 participants registered for the course. Among them, 76 (30%) completed all modules and both evaluations. The mean global knowledge score increased from 3.06/5 before to 4.21/5 after the MOOC (mean gain + 1.15, + 38%; P<.001). Improvements were consistent across all knowledge domains, including understanding of treatments (+40%), recognition of warning signs (+35%), and self-management skills (+39%). Overall, 98% (74/76) of respondents reported being satisfied or very satisfied with the course, and 99% (75/76) would recommend it to other patients. Since 2018, the MOOC has been conducted 6 times at different periods, enrolling a cumulative total of 2400 participants, confirming its sustainability and scalability. Conclusions: Participation in this co-designed, patient-centered MOOC was associated with a statistically and educationally significant improvement in knowledge among patients with MM and their caregivers. The program was highly valued by users and demonstrates the feasibility of large-scale digital patient education in oncology. As a freely accessible, repeatable, and peer-supported resource, this MOOC complements medical consultations and traditional TPE programs. Its design and outcomes may serve as a model for future digital health education initiatives targeting other chronic diseases.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 | 0,000 | 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 ».