Medical Student Perception of Plastic Surgery and the Impact of Mainstream Media
Bibliographic record
Abstract
BACKGROUND: Plastic surgery as a discipline is poorly understood by many, including primary care physicians, nurses, medical students, and the public. These misconceptions affect the specialty in a number of ways, including referral patterns and recruitment of medical students into residency programs. The reason for these commonly held misconceptions has not yet been addressed in the plastic surgery literature. As such, we assessed medical students' knowledge and perceptions of plastic surgery as a discipline and explored factors influencing these opinions. METHODS: To assess medical students' knowledge and perceptions of plastic surgery, we conducted an online survey. A total of 231 medical students responded. Interviews were then conducted with 2 focus groups, in which we explored the survey results and reasons behind these misconceptions. RESULTS: As with previous studies, medical students showed a gap in knowledge with respect to plastic surgery. Although they were generally aware that plastic surgeons perform cosmetic procedures and treat burns, they were largely unaware that plastic surgeons perform hand and craniofacial surgeries. Focus groups revealed that television plays a large role in shaping their ideas of plastic surgery. CONCLUSION: Medical students have a skewed perception of the discipline of plastic surgery, and this is largely influenced by television. Interventions aimed at educating medical students on the matter are recommended, including a greater presence in the preclerkship medical school curriculum.
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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 | 0.013 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".