A new wave of urologists? Graduating urology residents’ practices of and attitudes toward social media
Bibliographic record
Abstract
INTRODUCTION: Social media (SoMe) have revolutionized healthcare, but physicians remain hesitant to adopt SoMe in their practices. We sought to assess graduating urology residents' practices of and attitudes toward SoMe. METHODS: A close-ended questionnaire, employing five-point Likert scales, was distributed to all final-year residents (n=100) in Canadian urology training programs in 2012, 2014, and 2016 to assess SoMe usage and perceived usefulness. RESULTS: All (100%) questionnaires were completed. Respondents frequently used online services for personal (100%) and professional (96%) purposes. Most (92%) used SoMe. Many (73%) frequently used SoMe for personal purposes, but few (12%) frequently used SoMe for professional purposes. While a majority (59%) opposed direct patient interaction online, most supported using SoMe to provide patients with static information (76%) and collaborate with colleagues (65%). Many (70-73%) were optimistic that novel solutions to privacy issues in online communications will arise, making SoMe and email contact with patients conceivable. Few (2-8%) were aware and had read guidelines and legislations regarding physician online practices; however, awareness of medical associations' and institutional SoMe policies significantly increased over time (p<0.05). CONCLUSIONS: Despite their active online use, graduating urology residents rarely used SoMe in professional settings and were wary of using it in patient care. Nevertheless, they were optimistic toward its integration in urology and supported its use in physician-physician communication. Considering SoMe's increased influence on urology and graduating residents' limited awareness of guidelines and legislations, postgraduate medical educators should encourage residents to become more familiar with current online communication recommendations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".