Taking a Leap of Faith: A Study of Abruptly Transitioning an Undergraduate Medical Education Program to Distance-Learning Owing to the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has forced universities worldwide to immediately transition to distance-learning. Although numerous studies have investigated the effect of the COVID-19 pandemic on universities in the Middle East, none have reflected on the process through which medical education programs for health professions underwent this transition. This study aimed to elucidate the rapid transition to distance-learning of an undergraduate medical program at the College of Medicine, Mohammad Bin Rashid University of Medicine and Health Sciences (Dubai, United Arab Emirates), owing to the COVID-19 pandemic. An action research approach constituted the foundation of this collaborative effort that involved investigations, reflections, and improvements of practice, through ongoing cycles of planning, acting, observing, and reflecting. Efforts of transitioning to distance-learning were grouped into four interrelated aspects: supporting faculty members in delivering the program content, managing curriculum changes, engaging with the students to facilitate distance-learning experiences, and conducting web-based assessments. Challenges included the high perceived uncertainty, need for making ad hoc decisions, lack of experiential learning and testing of clinical skills, and blurring of work-life boundaries. Our preliminary findings show the successful generation of a strong existing digital base, future prospects for innovation, and a cohesive team that was key to agility, rapid decision-making, and program implementation.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".