Questions of Well-Being and Inclusion in Online Undergraduate Medical Education During COVID-19: A 2-Phased Focused Scoping Review
Bibliographic record
Abstract
PURPOSE: Undergraduate medical education (UGME) was transformed by the rapid move to online curriculum delivery during the COVID-19 pandemic. Most research on online UGME has focused on measuring its effectiveness. However, medical educators also require evidence regarding its implications with respect to well-being and inclusion. METHOD: To explore online learning transition, particularly the effect on well-being and inclusion, this 2-phased focused scoping review employed a revised Joanna Briggs Institute approach: (1) developing review questions and objectives; (2) determining eligibility criteria; (3) developing the search strategy; (4) extracting, analyzing, and discussing findings; (5) drawing conclusions; and (6) discussing implications for practice and further research. RESULTS: The review ultimately included 217 articles, of which 107 (49%) explored student and staff well-being during online UGME, 64 (30%) focused on inclusion in this context, and 46 (21%) examined both well-being and inclusion. Additionally, 137 of included articles (63%) were research/evaluation, 48 (22%) were descriptive, and 32 (15%) were opinion. Of the 137 research/evaluation studies, 115 (84%) were quantitative, 10 (7%) were qualitative, 8 (6%) were mixed methods, and 4 (3%) were Reviews. Among these research/evaluation studies, the most commonly used data collection method was surveys (n = 120), followed by academic tests (n = 14). Other methods included interviews (n = 6), focus groups (n = 4), written reflections (n = 3), user data (n = 1), and blood tests (n = 1). CONCLUSIONS: Important questions remain regarding the safety and inclusiveness of online learning spaces and approaches, particularly for members of historically excluded groups and learners in low-resource settings. More rigorous, theoretically informed research in online medical education is required to better understand the social implications of online medical education, including more in-depth, qualitative investigations about well-being and inclusion and more strategies for ensuring online spaces are safe, inclusive, and supportive.
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 | 0.153 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".