An Inquiry-Based Distance Learning Tool for Medical Students Under Lockdown (“COVID-19 Rounds”): Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic presented significant challenges to both clinical practice and the delivery of medical education. Educators and learners implemented novel techniques, including distance learning and web-based rounds, while trying to stay updated with the surge of information regarding COVID-19 epidemiology, pathogenesis, and treatment. Hence, we designed and implemented a technologically enhanced course called "COVID-19 Rounds" to educate students about the rapidly evolving pandemic. OBJECTIVE: The objectives of this study are to describe a technologically enhanced course called "COVID-19 Rounds" and evaluate the following: (1) student satisfaction and program usefulness in achieving preset objectives, (2) perceived improvement in literacy regarding the pandemic, and (3) the impact of student engagement by designing infographics and initiating COVID-19-related research projects. METHODS: This is a cross-sectional study measuring the impact of the implementation of the web-based "COVID-19 Rounds" course. This program included web-based clinical experiences with physicians on actual rounds in COVID-19 wards in the hospital, weekly updates on evolving data and new research, and engagement in student-led projects. The study population included 47 fourth-year medical students at the Mohamed Bin Rashid University of Medicine and Health Sciences in Dubai, the United Arab Emirates, who attended the course. We designed and administered a 47-item survey to assess student satisfaction, program usefulness, impact on knowledge, and student engagement. Data were collected at the end of program delivery via Microsoft Forms. RESULTS: In total, 38 (81%) out of 47 fourth-year medical students participated in this study. The final course evaluation revealed an overall high satisfaction rate, with a mean rating of 3.9 (SD 0.94) on the 5-point Likert scale. Most students were satisfied with the course format (27/38, 71%), organization (31/38, 82%), and the learning experience (28/38, 74%) that the course offered. The course was particularly appreciated for offering evidence-based talks about aspects of the pandemic (34/38, 90%), providing weekly updates regarding emerging evidence (32/38, 84%), and enhancing understanding of the challenges of the pandemic (34/38, 90%). Satisfaction with distance learning was moderate (23/37, 62%), and a minority of students would have preferred an in-person version of the course (10/37, 27%). Student engagement in the course was high. All students participated in small group presentations of infographics of pandemic-related topics. Perceived advantages included conciseness and visual appeal, and disadvantages included the lack of detail and the time-consuming nature of infographic design, especially for students with no prior design experience. After the course ended, 27 (57%) students began research projects. This resulted in 6 abstracts presented at local meetings and 8 scientific papers published or submitted for publication. CONCLUSIONS: This inquiry-based adaptive approach to educating medical students about updates on COVID-19 via web-based learning was successful in achieving objectives and encouraging engagement in research. However, shortcomings of the course related to the lack of in-person teaching and clinical activities were also highlighted.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".