A cross-sectional study to assess job strain of emergency healthcare workers by Karasek questionnaire: The SEEK study
Bibliographic record
Abstract
Background Emergency healthcare workers (eHCWs) are particularly at risk of stress, but data using the gold standard questionnaire of Karasek are scarce. We assessed the level of stress of eHCWs and aimed to compare it with the general population. Methods This is a cross-sectional nationwide study in French Emergency Departments (EDs), using the job-content questionnaire of Karasek, compared with the 25,000 answers in the French general population (controls from the SUMER study). The descriptions of job demand, job control, and social support were described as well as the prevalence of job strain and isostrain. Putative factors were searched using mixed-method analysis. Results A total of 166 eHCWs (37.9 ± 10.5 years old, 42% men) from five French EDs were included: 53 emergency physicians and 104 emergency paramedics, compared to 25,000 workers with other occupations. Job demand was highest for physicians (28.3 ± 3.3) and paramedics (25.9 ± 3.8), compared to controls (36.0 ± 7.2; p < 0.001). Job control was the lowest for physicians (61.2 ± 5.8) and paramedics (59.1 ± 6.8), compared to controls (70.4 ± 11.7; p < 0.001). Mean social support did not differ between groups (23.6 ± 3.4 for physicians, 22.6 ± 2.9 for paramedics, and 23.7 ± 3.6 for controls). The prevalence of job strain was massively higher for physicians (95.8%) and paramedics (84.8%), compared to controls (23.9%; p < 0.001), as well as for isostrain (45.1% for physicians, 56.8% for paramedics, and 14.3% for controls, p < 0.001). We did not find any significant impact of sociodemographic characteristics on job control, job demand, or social support. Conclusion Emergency healthcare workers have a dramatic rate of job strain, necessitating urgent promotion of policy to take care of them.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".