What Does It Take to Become an Academic Plastic Surgeon in Canada: Hiring Trends Over the Last 50 Years
Bibliographic record
Abstract
Objective: Academic plastic surgery positions have become highly competitive secondary to delayed retirement, stagnant hospital funding, and an increasing number of plastic surgery graduates. Little information is available to help residents navigate this challenging landscape. Our objectives were to evaluate the training backgrounds of all Canadian academic plastic surgeons and to develop recommendations for residents interested in an academic career. Methods: All Canadian academic plastic surgeons were included. Training histories were obtained from institutions’ websites. Surgeons were subsequently emailed to confirm this information and complete missing details. Multivariate regressions were designed to analyze the effect of gender and FRCSC year on graduate and fellowship training and time to first academic position. Results: Training information was available for 196 surgeons (22% female), with a 56% email response rate; 91% of surgeons completed residency in Canada; 94% completed fellowship training, while 43% held graduate degrees; 74% were employed where they previously trained. Female gender significantly lengthened the time from graduation to first academic job, despite equal qualification. Younger surgeons were more likely to hold graduate degrees ( P < .01). Conclusions: We identified objective data that correlate with being hired at an academic centre, including training at the same institution, obtaining a graduate degree during residency, and pursuing fellowship training. In addition, we demonstrated that women take significantly longer to acquire academic positions ( P < .01), despite equal qualification. Trainees should consider these patterns when planning their careers. Future research should explore gender-based discrepancies in hiring practices.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".