Physician wellness in orthopaedic surgery
Bibliographic record
Abstract
AIMS: Physician burnout and its consequences have been recognized as increasingly prevalent and important issues for both organizations and individuals involved in healthcare delivery. The purpose of this study was to describe and compare the patterns of self-reported wellness in orthopaedic surgeons and trainees from multiple nations with varying health systems. METHODS: A cross-sectional survey of 774 orthopaedic surgeons and trainees in five countries (Australia, Canada, New Zealand, UK, and USA) was conducted in 2019. Respondents were asked to complete the Mayo Clinic Well-Being Index and the Stanford Professional Fulfillment Index in addition to 31 personal/demographic questions and 27 employment-related questions via an anonymous online survey. RESULTS: A total of 684 participants from five countries (Australia (n = 74), Canada (n = 90), New Zealand (n = 69), UK (n = 105), and USA (n = 346)) completed both of the risk assessment questionnaires (Mayo and Stanford). Of these, 42.8% (n = 293) were trainees and 57.2% (n = 391) were attending surgeons. On the Mayo Clinic Well-Being Index, 58.6% of the overall sample reported feeling burned out (n = 401). Significant differences were found between nations with regards to the proportion categorized as being at risk for poor outcomes (27.5% for New Zealand (19/69) vs 54.4% for Canada (49/90) ; p = 0.001). On the Stanford Professional Fulfillment Index, 38.9% of the respondents were classified as being burned out (266/684). Prevalence of burnout ranged from 27% for Australia (20/74 up to 47.8% for Canadian respondents (43/90; p = 0.010). Younger age groups (20 to 29: RR 2.52 (95% confidence interval (CI) 1.39 to 4.58; p = 0.002); 30 to 39: RR 2.40 (95% CI 1.36 to 4.24; p = 0.003); 40 to 49: RR 2.30 (95% CI 1.35 to 3.9; p = 0.002)) and trainee status (RR 1.53 (95% CI 1.15 to 2.03 p = 0.004)) were independently associated with increased relative risk of having a 'at-risk' or 'burnout' score. CONCLUSIONS: 2021;2(11):932-939.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".