The Impact of Social Media on Plastic Surgery Residency Applicants
Bibliographic record
Abstract
BACKGROUND: Plastic surgeons have been early adopters of social media, and the efficacy and ethics of this practice have been studied. In addition, plastic and reconstructive surgery (PRS) training programs have begun using social media to connect with the public, including prospective PRS applicants. The ability of social media to attract prospective residency applicants is unknown. This study aims to examine the influence of social media on prospective residency applicants and their perception of a plastic surgery program. METHODS: In the academic years 2018 and 2019, we conducted an anonymous, voluntary survey among applicants applying to both the integrated and independent Harvard PRS residency programs. The survey collected data regarding demographics, social media usage, online information gathering, and PRS programs' social media influence on applicants' perception/rank position of programs. RESULTS: One hundred nine surveys were completed (23%). Ninety-seven percent of respondents reported searching online for information about residency programs. Twenty percent of respondents noted that a residency program's social media platform "influenced their perception of a program or intended rank position of a program" and 72% of those respondents indicated a positive effect on their perception of a program and its rank list position. At least 15% of respondents were concerned that engaging with a program's social media account would attract attention to their own social media accounts. CONCLUSIONS: Applicants routinely rely on online resources to gather information regarding prospective residency programs. Fear of attracting attention to their own personal social media pages may limit applicants' engagement with PRS programs on social media. However, residency programs can still utilize social media to deliver important messages, especially as social media usage continues to grow.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".