Why Do Early-Career Adult Reconstruction Surgeons Change Jobs? An American Association of Hip and Knee Surgeons Young Arthroplasty Group Survey Study
Bibliographic record
Abstract
Background There are high reported rates of burnout and job turnover among orthopedic surgeons. The purpose of this study was to investigate the prevalence of job change among early-career adult reconstruction surgeons and to examine which demographic or practice factors influenced job change. Methods An electronic survey was distributed to all practicing surgeon members of the American Association of Hip and Knee Surgeons Young Arthroplasty Group. The survey included questions about practice type, demographics, job change, and a validated burnout questionnaire. Survey responses were collected using a secure database. Statistical analysis was performed to examine relationships between respondent characteristics and job change. Results There were 201/389 responses (51.7%). The most common motivators for job change were better workplace culture (64%), opportunities for career growth (52%), and better alignment with values of the department/institution (45%). There were few female respondents; however, they trended toward reporting higher rates of job change (35.6% female vs 21.3% male, P = .3). Respondents who were considering changing jobs but had not done so were significantly more likely to report symptoms of burnout in all studied subscales: emotional exhaustion ( P < .0001), depersonalization ( P = .0002), and sense of personal accomplishment ( P = .007). Conclusions Surgeons changing jobs cited social factors such as workplace culture as reasons for leaving. Burnout symptoms were higher in surgeons considering changing jobs but improved in those who had already changed jobs. It is important to identify factors that lead to job change to guide young surgeons in job selection and improve retention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".