Online Learning in Medical Student Clerkship: A Survey of Student Perceptions and Future Directions
Bibliographic record
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic had a major impact on medical education with clerkship students abruptly removed from clinical activities in 2020 and hastily immersed in online learning to maintain medical education. In 2022, students returned to in-person clinical experiences, but synchronous learning sessions continued online with extensive use of asynchronous online resources. This change offers a unique opportunity to gather information about students' perspectives regarding the acceptability and effectiveness of online learning strategies. This study aims to explore the clerkship student experience with the integration of online learning and in-person learning into formalized educational sessions in clerkship. Methodology The authors administered an online survey to clerkship students at the Cumming School of Medicine at the University of Calgary, Canada in spring 2022. The survey consisted of primarily Likert-style questions to explore the perceived effectiveness of various online learning strategies. Results are reported as the proportion selecting "quite effective" or "extremely effective." Results A total of 89 students responded to the survey (57.4% of graduating class). For synchronous online learning, case-based learning was perceived as the most effective teaching strategy (61.8%), and audience response systems were the most effective strategy for improving audience engagement (70.1%). For asynchronous online learning, interactive cases (84.9%) and student-developed online study guides (83.6%) were perceived as the most effective. Students held varying perceptions regarding how online learning impacted their well-being. When considering future clerkship curricula, the majority of clerkship students preferred a blend of in-person and online learning. Conclusions This study identified that most clerkship students prefer a hybrid of in-person and online learning and that ideal online learning curricula could include case-based learning, audience response systems, and a variety of asynchronous learning resources. These results can guide curriculum development and design at other medical institutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".