Comparison of perceived educational value of an in-person versus virtual medical conference
Bibliographic record
Abstract
Purpose: Though prior literature has shown that virtual conferences improve accessibility and provide a comparable educational experience, further research is required to characterize their educational value. Methods: In this repeated cross-sectional study, demographic and survey data were compared between attendance perspectives for the in-person student-led internal medicine conference held in 2019 and subsequent virtual conference held in 2020. Results: There were 146 attendees at the in-person conference and 200 attendees at the online conference, in which 32 (22% response rate) and 52 responses (26% response rate) were gathered, respectively. Comparison of Likert Scale data via Mann-Whitney U Test revealed that learning objectives were better met in-person for the overall conference (p < 0.01) and didactic sessions (p < .05), but not for workshops, in which there was no significant difference. Survey takers noted the virtual conference to be more accessible on multiple factors, but felt as though their potential for interaction with other participants was more limited. Conclusions: Results indicate that though the virtual conference appeared more accessible to attendees, overall learning objectives for the conference and didactic sessions were better met in-person. Interestingly however, there was no observed difference in perceived educational value for small group workshops.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".