Advanced Training and Job Satisfaction Among Recent Canadian Plastic Surgery Graduates
Bibliographic record
Abstract
Background: In order to increase one’s competitiveness in the current job market, Canadian plastic surgery graduates may complete additional degrees and multiple fellowships. The authors sought to determine the impact of this additional training on the practice profile of recent graduates and determine the current state of job satisfaction among this group. Methods: An anonymous cross-sectional online survey was created and sent to all 250 graduates of Canadian plastic surgery residencies from 2005 to 2015. Demographics were collected and questions grouped into clinical, teaching, research, and administrative components. Questions pertaining to job satisfaction were also included. Results: The response rate to the survey was 39%. Sixty-nine (71%) respondents had permanent attending positions at the time of survey completion, while the remaining 28 respondents did not. Among those with permanent positions, 59 (86%) completed at least one fellowship and 30 (43%) have an advanced degree. Of those who did fellowship training, 76% practice primarily in their area of subspecialty. Having an advanced degree showed a trend to a higher percentage of practice dedicated to research (5.6% vs 1.9%; P = .074) and more publications per year were seen among this group (1.31 vs 0.30; P = .028). Eighty-six percent of respondents are satisfied with their current attending position. Conclusions: The majority of recent Canadian plastic surgery graduates are undergoing fellowship training and are practicing primarily in their fields of subspecialty training. Having a postgraduate degree was associated with a higher number of publications per year as an attending surgeon. Job satisfaction is high among recent graduates.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".