Telemedicine Enables Broader Access to Movement Disorders Curricula for Medical Students
Bibliographic record
Abstract
Background: The impact of tele-education for movement disorders on medical students is unknown. The present study had three objectives. First, to create a tele-education program for medical students in regions with limited access to movement disorders curricula. Second, to analyze the feasibility, satisfaction, and improvement of medical knowledge. Third, to assess the main reasons of medical students for attending this course. Methods: In 2016, a program was piloted in a low-middle income (Cameroon) and a middle-high income (Argentina) country. Medical students were offered a free movement disorder tele-education program (four medical schools in Argentina, and 1 medical school in Cameroon). Six real-time videoconferences covering hyperkinetic and hypokinetic movement disorders were included. Evaluations included attendance, pre- and post-medical knowledge, and satisfaction questionnaires. Results: The study included 151 undergraduate medical students (79.4% from Argentina, 20.6% from Cameroon). Feasibility was acceptable with 100% and 85.7% of the videoconferences completed in Argentina and Cameroon, respectively. Attendance was higher in Argentina compared to Cameroon (75% vs. 33.1%). According to student reports, the topics and innovative educational environment were the main reasons for attendance. Both groups ranked satisfaction as moderate to high, and medical knowledge improved similarly in both countries. Discussion: Tele-education can improve movement disorders knowledge in medical schools in high-middle and low-middle income countries lacking access to other educational opportunities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".