Distress in orthopedic trainees and attending surgeons: a Canadian national survey
Bibliographic record
Abstract
Background: Physician health is of increasing concern in health care systems. The purpose of this study was to determine the prevalence of distress among orthopedic surgeons and trainees and to identify factors associated with distress. Methods: Voluntary, anonymous online surveys were sent to attending orthopedic surgeons and orthopedic trainees across Canada. The survey for attending surgeons used the Expanded Physician Well-Being Index, and the survey for trainees used the Resident/Fellow Well-Being Index. Demographic information was also collected. To look for predictors of physician distress, we evaluated the relationship between respondents' classification as "distressed" and "not distressed" against demographic factors. Results: In total, 1138 attending orthopedic surgeons and 493 orthopedic trainees were invited to complete the survey. The survey response rate was 31.2% for attending orthopedic surgeons and 24.3% for orthopedic trainees. Overall, 55.4% of attending surgeons and 40.0% of trainees screened positive for distress. Among both attending surgeons and trainees, having dependents was not a risk factor for distress, nor was gender. Practice location was not a risk factor for distress among attending surgeons. Attending surgeons who were classified as distressed had spent significantly fewer years in practice (median 11 yr) than those who were classified as "not distressed" (median 16 yr) (p = 0.004). Conclusion: We found a higher rate of distress among orthopedic surgeons than has been previously reported. The distress rate among orthopedic trainees in this population is similar to that reported in other international publications, although self-reported rates of burnout were higher. The findings from this study may indicate a need for continuing research to determine intrinsic and extrinsic risk factors for distress among orthopedic surgeons and trainees and for the evaluation of prescriptive, evidence-based initiatives to address this crisis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".