Career Satisfaction Among General Surgeons in Canada
Bibliographic record
Abstract
PURPOSE: To understand what influences career satisfaction among general surgeons in urban and rural areas in Canada in order to improve recruitment and retention in general surgery. METHOD: Semistructured interviews were conducted with 32 general surgeons in 2010 who were members of the Canadian Association of General Surgeons and who currently practice in either an urban or rural area. Interviews explored factors contributing to career satisfaction, as well as suggestions for preventive, screening, or management strategies to support general surgery practice. RESULTS: Findings revealed that both urban and rural general surgeons experienced the most satisfaction from their ability to resolve patient problems quickly and effectively, enhancing their sense of the meaningfulness of their clinical practice. The supportive relationships with colleagues, trainees, and patients was also cited as a key source of career satisfaction. Conversely, insufficient access to resources and a perceived disconnect between hospital administration and clinical practice priorities were raised as key "systems-level" problems. As a result, many participants felt alienated from their work by these systems-level barriers that were perceived to hinder the provision of high-quality patient care. CONCLUSIONS: Career satisfaction among both urban and rural general surgeons was influenced positively by the social aspects of their work, such as patient and colleague relationships, as well as a perception of an increasing amount of control and autonomy over their professional commitments. The modern general surgeon values a balance between professional obligations and personal time that may be difficult to achieve given the current system constraints.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".