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)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".