Distance Electronic Learning Strategy in Medical Teaching During the COVID-19 Pandemic: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Teaching hospitals have been regarded as the primary settings where doctors teach and practice high-quality medicine, as well as where medical students learn the profession and acquire their initial clinical skills. A percentage of instruction is now done over the internet or via electronic techniques. The present COVID-19 epidemic has pushed distance electronic learning (DEL) to the forefront of education at all levels, including medical institutions. OBJECTIVE: This study aimed to observe how late-stage medical students felt about DEL, which was put in place during the recent COVID-19 shutdown in Jordan. METHODS: We conducted a prospective, cross-sectional, web-based, questionnaire-based research study during the COVID-19 pandemic lockdown between March 15 and May 1, 2020. During this period, all medical schools in Jordan shifted to DEL. RESULTS: A total of 380 students responded to a request to fill out the questionnaire, of which 256 completed the questionnaire. The data analysis showed that 43.6% (n=112) of respondents had no DEL experience, and 53.1% (n=136)of respondents perceived the DEL method as user-friendly. On the other hand, 64.1% (n=164) of students strongly believed that DEL cannot substitute traditional clinical teaching. There was a significant positive correlation between the perception of user-friendliness and the clarity of the images and texts used. Moreover, there was a strong positive correlation between the perception of sound audibility and confidence in applying knowledge gained through DEL to clinical practice. CONCLUSIONS: DEL is a necessary and important tool in modern medical education, but it should be used as an auxiliary approach in the clinical setting since it cannot replace conventional personal instruction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".