Overcoming barriers in access to ophthalmic education with virtual learning
Bibliographic record
Abstract
We would like to congratulate Dr Chatziralli et al. for their thought-provoking study on virtual education for ophthalmology during COVID-19. In this cross-sectional survey study, Dr Chatziralli and her team found a significant increase in the use of e-learning alternatives during the pandemic [ 1 ]. Medical training relies on the continued succession of learners, and as such, there is a critical need to minimize disruptions in this process to avoid negative downstream sequelae. Thus, learners and educators alike are reimagining contemporary medical education with a “forward thinking and scholarly approach” in adopting virtual pedagogies for acquiring clinical knowledge and skills [ 2 ]. Recently, we conducted a study to assess the efficacy of interactive virtual ophthalmology education for medical students during COVID-19. We found that interactive webinars and modules can successfully recreate clinically immersive learning environments while maintaining social distancing. Furthermore, we found that such open-access virtual platforms may mitigate inequities in accessing high-quality medical education and also reduce feelings of social isolation among learners [ 3 ]. Our findings are applicable beyond the context of COVID-19—globally, medical students report not gaining sufficient exposure to ophthalmology through formal medical education, and virtual learning may help to bridge this gap. Moreover, the authors argue that a “significant and inescapable disadvantage of the shift online is the restriction of professional networking and opportunities for ‘in person’ collaboration.” [ 1 ] Of course, this has implications for personal and professional development among the next generation of physicians [ 4 ]. Medical students already comprise one of the world’s loneliest populations, and early reports suggest that the mental health effects of social distancing and the colloquially termed “Zoom fatigue” may be deepening feelings of isolation in this group [ 5 ]. COVID-19 has catapulted us into a future defined by virtually integrated medical education. How we choose to move forward in this paradigm may redefine what it means to be a medical student, doctor, and lifelong learner forever. Future studies should investigate the impact of this shift toward virtual learning on metrics of learner well-being and professional identity formation as well as a more nuanced understanding of how to best deliver medical education in a virtual setting.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.049 | 0.034 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".