Zoomification of medical education: can the rapid online educational responses to COVID-19 prepare us for another educational disruption? A scoping review
Bibliographic record
Abstract
Introduction: In response to the COVID-19 pandemic, educators have increasingly shifted delivery of medical education to online/distance learning. Given the rapid and heterogeneous nature of adaptations; it is unclear what interventions have been developed, which strategies and technologies have been leveraged, or, more importantly, the rationales given for designs. Capturing the content and skills that were shifted to online, the type of platforms used for the adaptations, as well as the pedagogies, theories, or conceptual frameworks used to inform the adapted educational deliveries can bolster continued improvement and sustainability of distance/online education while preparing medical education for future large-scale disruptions. Methods: We conducted a scoping review to map the rapid medical educational interventions that have been adapted or transitioned to online between December 2019 and August 2020. We searched MEDLINE, EMBASE, Education Source, CINAHL, and Web of Science for articles pertaining to COVID-19, online (distance) learning, and education for medical students, residents, and staff. We included primary research articles and reports describing adaptations of previous educational content to online learning. Results: From an initial 980 articles, we identified 208 studies for full-text screening and 100 articles for data extraction. The majority of the reported scholarship came from Western Countries and was published in clinical science journals. Cognitive content was the main type of content adapted (over psychomotor, or affective). More than half of the articles used a video-conferencing software as the platform to pivot their educational intervention into virtual. Unfortunately, most of the reported work did not disclose their rationale for choosing a platform. Of those that did, the majority chose technological solutions based on availability within their institutions. Similarly, most of the articles did not report the use of any pedagogy, theory, or framework to inform the educational adaptations.
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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 | 0.026 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".