Voices of conference attendees: how should future hybrid conferences be designed?
Bibliographic record
Abstract
BACKGROUND: With conference attendees having expressed preference for hybrid meeting formats (containing both in-person and virtual components), organisers are challenged to find the best combination of events for academic meetings. Better understanding what attendees prioritise in a hybrid conference should allow better planning and need fulfilment. METHODS: An online survey with closed and open-ended questions was distributed to registrants of an international virtual conference. Responses were then submitted to descriptive statistical analysis and directed content analysis. RESULTS: 823 surveys (Response Rate = 4.9%) were received. Of the 813 who expressed a preference, 56.9% (N = 463) desired hybrid conference formats in the future, 32.0% (N = 260) preferred in-person conferences and 11.1% (N = 90) preferred virtual conferences. Presuming a hybrid meeting could be adopted, 67.4% (461/684) preferred that virtual sessions take place both during the in-person conference and be spread throughout the year. To optimise in-person components of hybrid conferences, recommendations received from 503 respondents included: prioritising clinical skills sessions (26.2%, N = 132), live international expert presentations and discussions (15.7%, N = 79) and interaction between delegates (13.5%, N = 68). To optimise virtual components, recommendations received from 486 respondents included: prioritising a live streaming platform with international experts' presentations and discussions (24.3%, N = 118), clinical case discussions (19.8%, N = 96) and clinical update sessions (10.1%, N = 49). CONCLUSIONS: Attendees envision hybrid conferences in which organisers can enable the vital interaction between individuals during an in-person component (e.g., networking, viewing and improving clinical skills) while accessing virtual content at their convenience (e.g., online expert presentations with latest advancements, clinical case discussions and debates). Having accessible virtual sessions throughout the year, as well as live streaming during the in-person component of hybrid conferences, allows for opportunity to prolong learning beyond the conference days.
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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 | 0.035 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".