Implementation and Evaluation of a Virtual Rheumatology Training Program in East Africa
Bibliographic record
Abstract
OBJECTIVE: Access to rheumatology education and care is limited in many African countries, leading to suboptimal clinical care and poor outcomes for patients. Virtual education is a feasible means to deliver curricula remotely. A virtual rheumatology course for medical residents in sub-Saharan Africa was developed. We describe the course and its evaluation. METHODS: An annual 16-week virtual rheumatology program was delivered to internal medicine residents in Rwanda between 2021 and 2024. Lectures on core rheumatology topics were provided, in English, by an international faculty that included lecturers from Africa to ensure regionally relevant content. In 2023, the virtual course was supplemented by a weeklong in-person visit. Participants completed questionnaires to evaluate their experiences with the course, their confidence in evaluating rheumatologic conditions, and any recommendations for course improvement. Instructors evaluated their experiences with the course. Summary statistics and representative quotations are provided. RESULTS: Postcourse evaluations were available from 55 residents and 7 instructors. All residents who completed the questionnaires reported the lectures were useful. Many (22/54 [41%]) requested additional time for case discussions and in-person teaching. After the course, residents rated their confidence in assessing and managing rheumatologic cases as good (median 7/10 [range 4-10]). Conflicting clinical duties prevented most residents (42/55 [76%]) from attending all lectures. Instructors reported some challenges, especially insufficient interaction during virtual lectures. CONCLUSION: A virtual rheumatology course is a feasible means to deliver rheumatology education to medical trainees but does not replace the need for in-person education. The program is adaptable to other regions with limited rheumatology resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".