On the advantages and disadvantages of virtual continuing medical education: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: With the COVID-19 pandemic, most continuing medical education activities became virtual (VCME). The authors conducted a scoping review to synthesize the advantages and disadvantages of VCME to establish the impact of this approach on inequities that physicians face along the intersections of gender, race, and location of practice. Methods: Guided by the methodological framework of Arksey and O'Malley, the search included six databases and was limited to studies published between January 1991 to April 2021. Eligible studies included those related to accredited/non-accredited post-certification medical education, conferences, or meetings in a virtual setting focused on physicians. Numeric and inductive thematic analyses were performed. Results: 282 studies were included in the review. Salient advantages identified were convenience, favourable learning formats, collaboration opportunities, effectiveness at improving knowledge and clinical practices, and cost-effectiveness. Prominent disadvantages included technological barriers, poor design, cost, lack of sufficient technological skill, and time. Analysis of the studies showed that VCME was most common in the general/family practice specialty, in suburban settings, and held by countries in the Global North. A minority of studies reported on gender (35%) and race (4%). Discussion: Most studies report advantages of VCME, but disadvantages and barriers exist that are contextual to the location of practice and medical subspecialty. VCME events are largely organized by Global North countries with suboptimized accessibility for Global South attendees. A lack of reported data on gender and race reveals a limited understanding of how VCME affects vulnerable populations, prompting potential future considerations as it evolves.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 it