Analysis of suggestions and interventions in medical education during the COVID-19 pandemic
Bibliographic record
Abstract
Medical education was heavily impacted by the public health measures implemented due to COVID-19 pandemic. This literature review summarizes and discusses the strengths and limitations of novel medical education interventions/suggestions during the pandemic to assist medical institutions with the evaluation of various interventions before their implementation. A scoping review was conducted following the Arksey and O'Malley framework. MEDLINE and EMBASE were searched for publications from January 1st,2019-August 10th,2020 that proposed novel medical education interventions/suggestions during the pandemic. The search included MESH searches, titles, abstracts, and keywords of studies. Inclusion criteria was comprised of articles that: used quantitative, qualitative, or mixed method designs; included medical students as the primary study cohort; involved suggestions for new medical education strategies to accommodate for the COVID-19 changes; involved studies that assessed the challenges and strengths of new COVID-19 medical school interventions; were primary studies, reviews, published letters to an editor, or opinion pieces. A total of 54 articles were included in this review. Each article had one or more interventions proposed. 10 articles reported integrating medical students in the workforce. 7 articles discussed efforts to manage medical students’ stress. 5 articles described changes to the residency program application process. 10 articles discussed changes to examinations. 12 articles discussed changes clinical rotations and electives. 11 articles discussed implementing online clinical experience. 36 articles implemented or suggested online learning strategies. The literature review suggests that quantitative studies to assess the efficacy of each intervention is required given the differences in suggestions offered by institutions worldwide.
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 | 0.033 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".