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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".