Transition to Independent Surgical Practice and Burnout Among Early Career General Surgeons
Bibliographic record
Abstract
Background: The transition from surgical residency to independent practice is a challenging period that has not been well studied. Methods: An email invitation to complete a 55-item survey and the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) was sent to early career general surgeons across Canada. The chi-square test or Fisher’s exact test was used to compare demographic and survey characteristics with burnout. Multivariable logistic regression was performed. Results: Of the 586 surgeons contacted, 88 responded (15%); 51/88 surgeons (58.0%) were classified as burnt out according to the MBI-HSS. Most surgeons (68.2%) were not confident in their abilities to handle the business aspect of practice. The majority (60.2%) believed that a transition to independent practice program would be beneficial to recent surgical graduates. Conclusions: Our data showed high prevalence of burnout among recently graduated general surgeons across Canada. Further, respondents were not confident in their managerial and administrative skills required to run a successful independent practice.
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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 | 0.006 |
| 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) | 0.002 | 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".