Cliques within the crowd: identifying medical conference attendee subgroups by their motivations for participation
Bibliographic record
Abstract
Conferences enable rapid information sharing and networking that are vital to career development within academic communities. Addressing diverse attendee needs is challenging and getting it wrong wastes resources and dampens enthusiasm for the field. This study explores whether, and how, motivations for attendance can be grouped in relation to preferences to offer guidance to organizers and attendees. A pragmatic constructivist case study approach using mixed methods was adopted. Semi-structured interviews completed with key informants underwent thematic analysis. Survey results outlining attendees' perspectives underwent cluster and factor analysis. Stakeholder interviews (n = 13) suggested attendees could be grouped by motivations predictable from level of specialisation in a field and past engagement with conferences. From n = 1229 returned questionnaires, motivations were clustered into three factors: learning, personal and social. Three groups of attendees were identified. Group 1 (n = 500; 40.7%) was motivated by all factors. Group 2 (n = 345; 28.1%) was mainly motivated by the learning factor. Group 3 (n = 188; 15.3%) scored the social factor highest for in-person conferences and the learning factor highest for virtual meetings. All three groups expressed a preference for hybrid conferences in the future. This study indicates that medical conference attendees can be clustered based on their learning, personal and social motivations for attendance. The taxonomy enables organizers to tailor conference formats with guidance on how to utilize hybrid conferences, thereby enabling better catering to attendees' desires for knowledge gain relative to networking.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".