Changing landscape of medical conferences: identifying the goals motivating virtual vs in-person participation
Bibliographic record
Abstract
Objectives: This study was aimed at improving clarity regarding the goals underlying motivation for attendance at international meetings to accommodate evolving needs. Methods: We performed a case study of a large international medical conference by undertaking (a) semi-structured interviews with 13 multi-disciplinary stakeholders, which underwent thematic analysis, and (b) surveys of 1229 conference attendees, which underwent descriptive statistical analysis and directed content analysis. Results: Interviews suggested scientific updates and networking are priorities for in-person formats whereas flexibility and reduced travel are priorities for virtual formats. Surveys suggested motivations for attending both in-person and virtual conferences included: scientific updates (81.3% and 85.4%, respectively) and advancements in patient care (76.6%, 78.2%). Social interaction (e.g., to meet experts 80.6% and make/deepen professional connections 69.3%) was highly rated for in-person meetings, but not virtual meetings (51.0% and 30.8%, respectively). 58.9% of attendees prefer future meetings to be hybrid, including both in-person and virtual formats. Conclusions: We found a disconnect between attendees' preferences and recommendations currently put forward as socially responsible in terms of climate, equity and diversity. Meeting organisers may need to educate others about the value and costs involved in hybrid formats. When hybrid formats are possible, our data provide guidance on what to prioritize during in-person components and how to combine those with the benefits of global accessibility and flexibility enabled by virtual technology.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".