How Should Social Media Be Used in Transplantation? A Survey of the American Society of Transplant Surgeons
Bibliographic record
Abstract
BACKGROUND: Social media platforms are increasingly used in surgery and have shown promise as effective tools to promote deceased donation and expand living donor transplantation. There is a growing need to understand how social media-driven communication is perceived by providers in the field of transplantation. METHODS: We surveyed 299 members of the American Society of Transplant Surgeons about their use of, attitudes toward, and perceptions of social media and analyzed relationships between responses and participant characteristics. RESULTS: Respondents used social media to communicate with: family and friends (76%), surgeons (59%), transplant professionals (57%), transplant recipients (21%), living donors (16%), and waitlisted candidates (15%). Most respondents (83%) reported using social media for at least 1 purpose. Although most (61%) supported sharing information with transplant recipients via social media, 42% believed it should not be used to facilitate living donor-recipient matching. Younger age (P = 0.02) and fewer years of experience in the field of transplantation (P = 0.03) were associated with stronger belief that social media can be influential in living organ donation. Respondents at transplant centers with higher reported use of social media had more favorable views about sharing information with transplant recipients (P < 0.01), increasing awareness about deceased organ donation (P < 0.01), and advertising for transplant centers (P < 0.01). Individual characteristics influence opinions about the role and clinical usefulness of social media. CONCLUSIONS: Transplant center involvement and support for social media may influence clinician perceptions and practices. Increasing use of social media among transplant professionals may provide an opportunity to deliver high-quality information to patients.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".