The differing perceptions of plastic surgery between potential applicants and current trainees: The importance of clinical exposure and electives for medical students
Bibliographic record
Abstract
BACKGROUND: Exposure to plastic surgery during medical school is limited. Most interested applicants form their perceptions of careers in this surgical specialty during elective rotations. OBJECTIVE: To investigate the perceptions of Canadian medical students considering a career in plastic surgery. The results obtained were then compared with current Canadian plastic surgery residents' perceptions. METHODS: The data were collected via two separate self-administered online surveys that were distributed to either Canadian plastic surgery residents or medical students. The questionnaires were similar and focused on three aspects: applicant details; driving force behind interest in the field; and essential character traits and competencies related to successful matching. RESULTS: Fifty-nine plastic surgery residents and 477 medical students participated in the online survey. The most commonly reported driving forces for interest in a plastic surgery career in both groups were variety of career choice, complexity of the field, future lifestyle and enjoyable rotations in plastic surgery. Despite these similarities, the proportion of medical students and residents who opted for future lifestyle and enjoyable rotations differed in a statistically significant manner (P=0.015 and P=0.029, respectively). In terms of the essential competencies to match into a plastic surgery training spot, the groups differed statistically in their opinions on the relevance of intellect (P<0.001), manual dexterity (P<0.001), spatial sense (P<0.001) and clerkship grades (P=0.004). CONCLUSION: Interested applicants should be encouraged to obtain as much elective experience as possible to assist both students in their career choice and selection committees in identifying capable applicants.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".