Perceptions, awareness and influences of medical students towards plastic surgery: A systematic review
Bibliographic record
Abstract
Introduction: Plastic, reconstructive and aesthetic surgery (PRAS) is a significant yet often overlooked specialty in medical school curricula. The impact of social media and unregulated information sources can distort the perceptions of medical specialties, including PRAS, leading to a decline in student interest, inappropriate referrals and strain on healthcare services. This systematic review aimed to understand the perceptions of medical students towards PRAS, identify influencing factors and explore strategies to address these influences. Methods: The review followed the PRISMA 2020 guidelines. Four databases were searched, and the inclusion and exclusion criteria were applied. Data from 17 relevant studies were analysed in Microsoft Excel using descriptive statistics. The risk of bias was assessed using a modified Newcastle-Ottawa Scale. Results: Medical students generally held positive perceptions about PRAS, particularly regarding career opportunities, specialised skills and the nature of the specialty. However, their awareness of the full scope of plastic surgery is limited, with a focus on cosmetic and aesthetic procedures. Social media and the internet significantly influenced the students' perceptions, whereas personal experiences had a minor impact. Education and training in plastic surgery positively affected the students' perceptions. Nevertheless, there is a need for improved representation of PRAS in medical school curricula and promotion of accurate information through reliable sources. Conclusion: Students exhibited a favourable attitude towards plastic surgery, but their knowledge of the specialty can be enhanced. Strengthening PRAS teaching in medical schools and ensuring accurate information dissemination can foster a deeper understanding and interest in this field. Large-scale studies with standardised protocols should be conducted in different countries to gain comprehensive insights tailored to specific educational contexts.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".