Psychosocial Stress Among Hospital Doctors in Surgical Fields
Bibliographic record
Abstract
BACKGROUND: The aim of this paper is to analyze psychosocial stress in the workplace among hospital doctors working in surgical fields in Germany with the aid of the demand-control model, the effort-reward imbalance model, and selected additional indicators. METHODS: A written questionnaire was answered by a stratified random sample consisting of 1311 hospital doctors working in surgical fields in 489 hospitals in Germany. Validated instruments were used to make measurements according to the demand-control and effort-reward imbalance models. RESULTS: The working conditions of about a quarter of the hospital doctors surveyed were characterized by an effort-reward imbalance. 22% of them have "job strain" according to the demand-control model, i.e., they are confronted with high demands, yet have a low degree of control. Residents and assistant physicians not occupying training positions were both found to have an especially high degree of psychosocial stress. Furthermore, about one-fifth of the hospital doctors surveyed thought about giving up their profession at least a few times per month. 44% of them considered that the quality of patient care was sometimes or often impaired by an excessive physician workload. CONCLUSION: An investigation of psychosocial stress in the workplace among hospital doctors in surgical fields in Germany indicates that this group suffers from more severe stress at work than other occupational groups. Such working conditions pose a threat to these physicians' own health and to the quality of the health care that they provide.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".