Novel survey disseminated through Twitter supports its utility for networking, disseminating research, advocacy, clinical practice and other professional goals
Bibliographic record
Abstract
INTRODUCTION: Twitter use has grown exponentially within the urological community. We aimed to determine the perceptions of the impact of Twitter on users' clinical practice, research, and other professional activities. METHODS: We performed an 11-item online survey of Twitter contributors during two major urological meetings: the European Association of Urology (EAU) and the American Urological Association (AUA) annual meetings. During the EAU 2014 meeting, we distributed the survey via the meeting official Twitter feed. During the AUA 2014 meeting, we applied a new method by directly sending the survey to Twitter contributors. We performed a subset analysis for assessing the perceived impact of Twitter on the clinical practice of physicians. RESULTS: Among 312 total respondents, the greatest perceived benefits of Twitter among users were for networking (97%) and disseminating information (96%), followed by research (75%), advocacy (74%) and career development (62%). In total, 65% of Twitter users have dealt with guidelines on online medical professionalism and 71% of physician users found that Twitter had an impact on their clinical practice, and 33% had made a clinical decision based on an online case discussion. CONCLUSIONS: Our results suggest that Twitter users in the urological community perceive important benefits. These benefits extend to multiple professional domains, particularly networking, disseminating information, remote conference participation, research, and advocacy. This is the first study that has been disseminated to targeted individuals from the urological community directly through tweets, providing a proof of principle for this research method.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".