Best practices for effective implementation of online teaching and learning in medical and health professions education: during COVID-19 and beyond
Bibliographic record
Abstract
The COVID-19 pandemic has caused worldwide disruption to the entire educational system, including medical and health professions education. Considering the critical situation due to COVID-19, academic institutions shifted the entire pedagogical approach to the virtual learning mode. While delivering online teaching, educators experienced numerous challenges, including access to the internet, poor connectivity, and other technical issues. Some students did not have laptops and necessary devices to attend the Class. Besides, many educators were not confident enough to manage the online mode of delivery. In this perspective, we reviewed the evidence of best practices for the medical and health professions educators to deliver the curriculum through an online platform. Therefore, the current study aimed to review the best practices for effective online teaching and learning in medical and health professions education during COVID-19 and beyond. We reviewed the technical aspects of online teaching and educational strategies required for educators to provide quality training not just during the pandemic but beyond this crisis. The online literature search was performed on Medline, PubMed and google scholar databases for studies on online teaching in medical and health profession education and what are the best practices of teaching globally Online teaching and assessment must balance the requirements of technology, learning outcomes, delivery modes, learning resources, and learning resources. The study concludes that medical and health professions institutions strengthen technical infrastructure, promote continuous faculty development programs, and support indigent students to access digital technology.
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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 | 0.047 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".