Online medical education: A student survey
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".