Faculty Development Interventions in Medical Education During the COVID-19 Pandemic: A Systematic Review
Bibliographic record
Abstract
The onset of the COVID-19 pandemic in March 2020 began a series of disruptions that rapidly impacted medical education across the globe. This review collates current literature that relates to medical education faculties’ development interventions as a result of the pandemic, with an emphasis on the effectiveness and type of strategies for intervention, such as the usefulness of hybrid and digitalised education. The study used PRISMA guidelines when conducting the literature survey with specified inclusion and exclusion criteria across numerous academic databases. From this survey, 1158 articles were found. The EndNote programme was used to identify and remove duplicate pieces. From this, 479 abstracts were reviewed. A total of 36 articles were selected for their relevance; from this, 11 were deemed to have met the inclusion criteria to warrant full-text analysis. To identify bias risk in these 11 studies, the Newcastle–Ottawa Scale (NOS) was utilised. The study findings have two major themes: (1) information about faculty development interventions; and (2) the nature of articles written in the pandemic. From this, it was indicated that faculty development initiatives are useful for improving teachers’ competency and for enhancing teachers’ adoption of digitalised learning to ultimately bolster the resilience of their teaching. The findings also show that there is a strong need to have robust frameworks in place for faculty development, and that such frameworks must be followed in and beyond the pandemic period to improve the long-term incorporation of online learning into medical education.
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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".