Physician wellbeing and burnout in emergency medicine in Switzerland
Bibliographic record
Abstract
Emergency physicians are the most at-risk medical specialist group for burnout. Given its consequences for patient care and physician health and its resulting increased attrition rates, ensuring the wellbeing of emergency physicians is vital for preserving the integrity of the safety net for the healthcare system that is emergency medicine. In an effort to understand the current state of practicing physicians, this study reviews the results of the first national e-survey on physician wellbeing and burnout in emergency medicine in Switzerland. Addressed to all emergency physicians between March and April 2023, it received 611 complete responses. More than half of respondents met at least one criterion for burnout according to the Maslach Burnout Inventory - Human Services Survey (59.2%) and the Copenhagen Burnout Inventory (54.1%). In addition, more than half reported symptoms suggestive of mild to severe depression, with close to 20% screening positively for moderate to severe depression, nearly 4 times the incidence in the general population, according to the Patient Health Questionnaire-9. We found that 10.8% of respondents reported having considered suicide at some point in their career, with nearly half having considered this in the previous 12 months. The resulting high attrition rates (40.6% of respondents had considered leaving emergency medicine because of their working conditions) call into question the sustainability of the system. Coinciding with trends observed in other international studies on burnout in emergency medicine, this study reinforces the fact that certain factors associated with wellbeing are intrinsic to emergency medicine working conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".