Parents’ Perceptions of Social Media Use by Pediatric Plastic Surgeons
Bibliographic record
Abstract
BACKGROUND: Pediatric craniofacial surgeons face specific ethical and professional concerns regarding the growth of social media use in plastic surgery. The authors sought to elucidate parents' views on social media use by pediatric craniofacial surgeons. In addition, the study examines issues of consent, assent, and exploitation. METHODS: An anonymous, 51-question, multiple-choice survey investigating parents' views of pediatric plastic surgeons' social media use was crowdsourced through Prolific.Co. RESULTS: A total of 681 parents responded to the survey. Of the 656 included respondents, 88% to 93% believe that surgeons need to obtain consent from the parents to post pictures of the pediatric patient on social media, regardless of age. Respectively, 84.5% and 75% of respondents believe the surgeon needs to document assent from the 12- and 10-year-old patient. Parents who follow plastic surgeons on social media are more likely to agree that assent needs to be documented from all pediatric patients, regardless of age, before posting pictures on social media. The mean age at which respondents believe physicians should be required to elicit assent from the pediatric patient to post pictures was 9.65 years. Approximately 40% of respondents felt that patients portrayed in pictures on social media were being exploited, irrespective of the child's stated age. CONCLUSIONS: Plastic surgeons need to recognize parents' perceptions regarding social media use when considering posting pictures of pediatric patients on social media. The data suggest that surgeons should elicit assent from patients as young as 9 years before posting pictures on social media.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".