Design and Development of an e-Learning Patient Education Program for Self-management Support in Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
Background Patient education is integral to the treatment and care of patients with rheumatoid arthritis. Furthermore, change is taking place in the organization of health care systems because of a demographic shift toward aging populations and advancements in digital technologies, allowing for new interventions. However, evidence on how to provide web-based patient education within arthritis is limited. Objective This study aimed to develop an e-learning education program targeting patients with rheumatoid arthritis. Methods The development involved content specification and creative design with contributions from the investigators, patient research partners, and experts in communication, digital design, and e-learning. It was theoretically framed within theories of self-management and behavior change, multimedia learning, and entertainment education and empirically based on the evidence of patient education in rheumatoid arthritis and focus group discussions with patients, nurses, and rheumatologists. Finally, we conducted a feasibility test among 10 patients to assess the acceptability and usability of the program to identify areas to be adjusted. Results The 5 following themes for educational needs were found in focus group discussions: “Knowledge of rheumatoid arthritis,” “The disease course and prognosis,” “Medical treatment,” “A new life situation,” and “Daily life with rheumatoid arthritis.” Based on these themes, a didactic and entertaining e-learning program with a simple user interface was created. It consists of 3 modules covering the disease course, examinations, treatment, and daily life with rheumatoid arthritis. It combines animations, videos, podcasts, text, speech, and tests. The patients who tested the program found it to be feasible—that is, clear in content and easy to understand with a suitable pace and coherence between graphics, speech, and text. Conclusions This e-learning program is based on solid theoretical knowledge that meets users’ needs and is easy to use. Our study describes possible elements integrated in the development of web-based educational tools that can guide future development processes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".