Prevalence of medical students’ satisfaction with online education during COVID- 19 pandemic: A systematic review and meta-analysis
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> </ns4:p> <ns4:p>Electronic (E)-learning is defined as the use of electronic tools for education, training, and communication.(ERT) is defined as use of alternative methods of teaching such as remote learning during a crisis circumstances Education, among many other sectors, has been profoundly affected by the spread of the coronavirus disease 2019 (COVID-19). More than 90% of the world’s students are unable to attend teaching sessions due to the COVID-19 pandemic.</ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> </ns4:p> <ns4:p>This study was conducted in accordance with the published guidelines for meta-analysis and reviews (PRISMA) reporting guidelines. A database and electronic search was performed on September 21st, 2021 using PubMed, Medline and Embase through the OVID platform, and ScienceDirect. We removed duplicates, and screened the title, abstract, and full texts of included papers. We included studies published only in English and excluded studies without sufficient data, case reports, editorials, and protocols. The quality of included articles was examined using the AXIS tool for cross-sectional studies, and the Newcastle–Ottawa scale for observational case-control studies. From the included studies, demographic and satisfaction with online education (OE) prevalence data were extracted and analyzed. We calculated the pooled prevalence of medical students’ satisfaction.</ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> </ns4:p> <ns4:p>Eighteen studies with a total sample of 7,907 students were included in the meta-analysis. The pooled prevalence of medical students’ satisfaction with online education was 57% (95% CI: 47 - 67%). Publication bias was assessed and reported.</ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> The pooled prevalence of medical students’ satisfaction with online education was 57 %. Online learning satisfaction was associated with students’ prior experience with OE. The greatest benefit of OE is overcoming obstacles faced with learning Major challenges for implementing OE were technical and infrastructural resources. </ns4:p>
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".