Changing landscape of medical conferences: identifying the goals motivating virtual vs in-person participation
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
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.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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