Insta-Grated Plastic Surgery Residencies: 2020 Update
Bibliographic record
Abstract
BACKGROUND: Recent evidence shows accelerating worldwide adoption of social media and suggests a commensurate increase in social media use by integrated plastic surgery residency programs in the United States. Programs nationwide are now making strides to include a longitudinal social media component in their plastic surgery curriculum. OBJECTIVES: The aim of this study was to investigate the use of Instagram by plastic surgery residency programs and to describe trends in adoption, volume, and content. METHODS: Current active Instagram accounts affiliated to integrated plastic surgery residency programs were surveyed to identify date of first post, number of posts, number of followers, number of followings, engagement rate, most-liked posts, and content of posts. All data were collected on May 12, 2020. RESULTS: Sixty-nine out of 81 (85.2%) integrated plastic surgery residency programs had Instagram accounts, totaling 5,544 posts. This represents an absolute increase in program accounts of 392% since 2018. The 100 most-liked posts were categorized as: promotion of the program/individual (46), resident life (32), promotion of plastic surgery (14), and education (8). CONCLUSIONS: Instagram use by plastic surgery residency programs has drastically increased since it was first evaluated in 2018. This trend will continue as we reach near saturation of residency programs with accounts. We remain steadfast in our belief that the advantages of social media use by plastic surgeons and trainees are far outweighed by the potential community-wide impacts of violations of good social media practice on peers, patients, and the general public.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".