How Social Are We? A Cross-Sectional Study of the Website Presence and Social Media Activity of Canadian Plastic Surgeons
Bibliographic record
Abstract
The internet and social media are increasingly being used by patients not only for health-related research, but also for obtaining information on their surgeon. Having an online presence via a website and social media profile is one-way plastic surgeons can meet this patient driven demand. The authors sought to document current website and social media usage of Canadian plastic surgeons and to determine if this usage correlated with years in practice. A Google search was performed using publicly available lists of all plastic surgeons registered with the Royal College of Physicians and Surgeons of Canada (RCPSC) and the Canadian Society for Aesthetic Plastic Surgery (CSAPS). This search found 42% (268/631) of RCPSC plastic surgeons had a website and 85% (536/631) had a profile on social media. Younger RCPSC surgeons (registered for less years) were significantly more likely to have a website (12.8 vs. 21.9 years, P < 0.0001) and an active social media profile (16.2 vs. 23.9 years, P < 0.002). The social media platform most used was RateMDs (81%) followed in decreasing order by: LinkedIn (28%), RealSelf (22%), Facebook (20%), Google+ (17%) and Twitter (16%). Dual RCPSC-CSAPS members were more likely than RCPSC-only members to have a website (56 vs. 36%, P < 0.0001) and an active social media profile (P < 0.05). Overall, current website usage and social media presence by Canadian plastic surgeons is comparable to counterparts in the US and UK. It may be possible to better optimize online presence through education of current search engine technology and becoming active on multiple social media platforms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".