Why Do General Surgeons Decide to Retire?
Bibliographic record
Abstract
: Limited recent data exist regarding intended retirement plans for general surgeons (GS). We sought to understand when and why surgeons decide to stop operating as primary surgeon and stop all clinical work.A paper-based survey of practicing GS in the province of Ontario, Canada, was conducted. A questionnaire was developed using a systematic approach of item generation and reduction. Face and content validity were tested. The survey was administered via mail, with a planned reminder.Overall response rate was 33.5% (242/723). The median age at which respondents planned to/did stop operating was 65 (interquartile range 60-67.5). The median age at which respondents planned to/did retire from all clinical work was 70 (interquartile range 65-72.5). Career satisfaction (97%), sense of identity (90%), and financial need (69%) were factors that influenced the decision to continue operating. Enjoyment of work (79%), camaraderie with surgical colleagues (66%), and financial need (45%) were reasons to continue working after ceasing to operate as the primary surgeon. On multivariate analysis, younger respondents (36-50 years old) perceived they were less likely to continue operating past age 65 (odds ratio 0.13), and academic surgeons were more likely to stop operating after age 65 (odds ratio 2.39). Call coverage by nonstaff surgeons was not associated with retirement age.Overall, GS plan to stop operating at age 65, and to cease all clinical activities at age 70. Younger, nonacademic surgeons plan to stop operating earlier. Career satisfaction, sense of identity, and financial need are the principal reported motivations to continue operating.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".