Social Media in Academic Plastic Surgery Training Programs: A 5-Year Update
Bibliographic record
Abstract
Purpose: Social media is an important tool for plastic and reconstructive surgery (PRS) training programs to engage prospective applicants. A study by the senior author (2020) describes an exponential increase in program presence on social media from 2010 to 2018. This study aims to provide an updated analysis of PRS training program utilization of social media in relation to prior results from our senior author. Materials and Methods: A national cross-sectional study of PRS programs was conducted. Instagram, Facebook, X, and TikTok were queried. Instagram posts were categorized by type. Factors influencing the number of followers were analyzed. Accounts were analyzed using Kruskal-Wallis, Jonckheere-Terpstra, independent-sample T test, and regression analysis. Results: All 89 integrated PRS programs have Instagram accounts, with 19,498 total posts. Posts most often highlight the program/curriculum (34.8%), social activities (13.1%), and research-related content (10.4%). Nearly a quarter of posts (23.1%) were categorized as “other” (eg, birthday posts and nonspecific pictures of residents). The percentage of research-related posts has declined since 2020. Active program accounts on X and Facebook are declining linearly ( R 2 =0.96 and 0.70, respectively). Only one program has a TikTok account. Impact of region and rank on followers was consistent with prior findings. Conclusions: Social media adoption by PRS programs has stabilized following a period of growth described in 2020. Instagram is the most popular platform; Facebook and X use is declining. Instagram posts primarily depict program structure, curriculum, and culture, with a third of posts being nonacademic in nature.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".