Nephrology Health Professionals’ Perspectives on Their Preferred Modality for Scientific Meetings, Balancing Affordability, Sustainability and Opportunities to Interact With Peers: An International Society of Nephrology Survey
Bibliographic record
Abstract
Introduction: The COVID-19 lockdowns and awareness of the ecological impact of our profession led us to reconsider the way we deliver scientific meetings. In this International Society of Nephrology (ISN) survey, we aimed to investigate health professionals' preferences for in-person, virtual or hybrid nephrology meetings. Methods: questionnaire was developed within the ISN Western Europe Regional Board; a pilot survey checked for clarity and consistency (204 professionals). All members of the ISN Working Group were contacted between March and May 2023. Quantitative and qualitative data were recorded and analyzed. Results: A total of 499 participants from 102 countries completed the survey. Most of them agreed that a combination of in-person, remote, and hybrid meetings works best, whereas 14.1% preferred virtual meetings only, and 25.2% preferred in-person meetings only. Hybrid meetings were identified as the best option for the future, regardless of the number of attendees. Flexibility in schedules was their most important advantage, even if this modality limits networking. In-person meetings allow for discussions with international peers, although costs may be a barrier. The wiser use of resources and better management of large numbers of attendees were the main advantages of hybrid meetings (about 30% each); however, this modality potentially discriminates on the basis of financial resources and geographical area (31%). Conclusion: Hybrid meetings, allowing for more flexibility and better utilization of resources, were the preferred modality for scientific meetings, regardless of the number of participants. A targeted survey could further explore how to optimize meeting attendance and participation in scientific discussions.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".