Widespread use of internet, applications, and social media in the professional life of urology residents
Bibliographic record
Abstract
INTRODUCTION: Digital media have revolutionized communication and information dissemination in healthcare. We aimed to quantify and evaluate professional digital media use among urology residents. METHODS: We designed a 17-item survey to assess usage and perceived usefulness of digital media, as well as communication type and device type and distributed it via email to 143 Canadian and 721 German urology residents. RESULTS: In total, 58 (41% response rate) residents from Canada and 170 (24% response rate) from Germany reported professional usage rates of 100% on the internet, 89% on apps, and 46% on social media (SoMe). For professional use, residents spent a median of 30 minutes per day on the internet, 10 minutes on apps, and 15 minutes on SoMe. 100% rated the internet, 89% apps, and 31% SoMe as useful for clinical practice. Most (94%) used digital media for communication with colleagues and 23% for communication with patients. Digital media use was allocated to desktop computers (55%) and mobile devices (45%). Canadian residents had higher usage rates of apps (96% vs. 86%; p=0.042) and SoMe (65% vs. 39%; p=0.002) and longer daily usage times for the internet, apps, and SoMe than German residents (p<0.001 each). CONCLUSIONS: Digital media are an integral part of the daily professional practice of urology residents, reflected by high usage rates and perceived usefulness of the internet and apps, and the growing importance of SoMe. Urologists should strive to progressively exhaust the vast potential of digital media for academic and clinical practice.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".