“We Won’t Retire Without Skeletons in the Closet”: Healthcare-Related Regrets Among Physicians and Nurses in German-Speaking Swiss Hospitals
Bibliographic record
Abstract
Physicians and nurses are expected to systematically provide high-quality healthcare in a context marked by complexity, time pressure, heavy workload, and the influence of nonclinical factors on clinical decisions. Therefore, healthcare professionals must eventually deal with unfortunate events to which regret is a typical emotional reaction. Using semistructured interviews, 11 physicians and 13 nurses working in two different hospitals in the German-speaking part of Switzerland reported a total of 48 healthcare-related regret experiences. Intense feelings of healthcare-related regrets had far-reaching repercussions on participants' health, work-life balance, and medical practice. Besides active compensation strategies, social capital was the most important coping resource. Receiving superiors' support was crucial for reaffirming professional identity and helped prevent healthcare professionals from quitting their job. Findings suggest that training targeting emotional coping could be beneficial for quality of life and may ultimately lead to lower job turnover among healthcare professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".