Twitter and academic Urology in the United States and Canada: a comprehensive assessment of the Twitterverse in 2019
Bibliographic record
Abstract
OBJECTIVE: To provide the first comprehensive analysis of the Twitterverse amongst academic urologists and programmes in North America. METHODS: Using national accreditation and individual programme websites, all active urology residency programmes (USA and Canada) and academic Urology faculty at these programmes were identified. Demographic data for each programme American Urological Association [AUA] section, resident class size) and physician (title, fellowship training, Scopus Hirsch index [H-index] and citations) were documented. Twitter metrics (Twitter handle, date joined, # tweets, # followers, # following, likes) for programmes and physicians were catalogued (data capture: March-April 2019). Descriptive analyses and temporal trends in Twitter utilisation amongst programmes and physicians were assessed. Multivariable logistic regression was used to identify predictors of Twitter use. RESULTS: In all, 156 academic programmes (143 USA, 13 Canada) and 2214 academic faculty (2015 USA, 199 Canada) were identified. Twitter utilisation is currently 49.3% and 34.1% amongst programmes and physicians, respectively, and continues to increase. On multivariable analysis, programmes with 3-5 residents/year and programmes with a higher percentage of faculty Twitter engagement were more likely to have Twitter accounts. From a physician perspective, those with fellowship training, lower academic rank (Clinical Instructor, Assistant Professor, Associate Professor vs Professor) and higher H-indices were more likely to have individual Twitter accounts. CONCLUSION: There is a steady increase in Twitter engagement amongst Urology programmes and academic physicians. Faculty Twitter utilisation is an important driver of programme Twitter engagement. Twitter social media activity is strongly associated with academic productivity, and may in fact drive academic metrics. Within Urology, social media presence appears to be proportional to academic activity.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".