Online medical education: A student survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: During COVID-19, medical schools transitioned to online learning as an emergency response to deliver their education programmes. This multi-country study compared the methods by which medical schools worldwide restructured the delivery of medical education during the pandemic. METHODS: This multi-country, cross-sectional study was performed using an internet-based survey distributed to medical students in multiple languages in November 2020. RESULTS: A total of 1,746 responses were received from 79 countries. Most respondents reported that their institution stopped in-person lectures, ranging from 74% in low-income countries (LICs) to 93% in upper-middle-income countries. While only 36% of respondents reported that their medical school used online learning before the pandemic, 93% reported using online learning after the pandemic started. Of students enrolled in clinical rotations, 89% reported that their rotations were paused during the pandemic. Online learning replaced in-person clinical rotations for 32% of respondents from LICs versus 55% from high-income countries (HICs). Forty-three per cent of students from LICs reported that their internet connection was insufficient for online learning, compared to 11% in HICs. CONCLUSIONS: The transition to online learning due to COVID-19 impacted medical education worldwide. However, this impact varied among countries of different income levels, with students from LICs and lower middle income countries facing greater challenges in accessing online medical education opportunities while in-person learning was halted. Specific policies and resources are needed to ensure equitable access to online learning for medical students in all countries, regardless of socioeconomic status.
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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 | 0.007 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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 it