Factors Influencing Practice Decisions Among Plastic Surgery Residents and Early-Career Plastic Surgeons in Canada
Bibliographic record
Abstract
Introduction: Trends within the Canadian plastic surgery workforce demonstrated that most work in academic or medium-large community practice. Recent studies observed more plastic surgeons are incorporating aesthetics into their practice. This study aims to identify factors influencing how plastic surgery residents and early-career plastic surgeons in Canada choose their eventual practice with respect to practice type and practice location. Methods: A REDCap survey was distributed to plastic surgery residents and early-career plastic surgeons across Canada between February and May 2024. Demographics, training information, career information, and Likert-scale questions for factors involved in decision-making were surveyed. Data analysis included descriptive statistics and Fisher exact tests. Results: There were 45 residents and 30 early-career plastic surgeon respondents. Mixed practices that included aesthetics were the most popular practice types among residents (73%) and early-career surgeons (77%). Half (53%) of early-career surgeons were working in urban settings with more than 1 million people, and 44% of residents desired these locations. Hometown factors heavily influenced practice type and location ( P < .0001), more than training experiences. Positive interactions (94%), operating time (90%), partner opinion (87%), and hospital resources (86%) were ranked as the most important factors involved in practice decisions. Conclusion: Mixed practices that include aesthetics were the most popular among our up-and-coming Canadian workforce, especially in urban settings. These changing practice trends may impact our ability as a specialty to adequately meet the needs of the Canadian population. Recruitment efforts should focus on promoting a supportive workplace and local environment, with adequate operating time and resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".