Burnout Among Hospital Non-Healthcare Staff
Bibliographic record
Abstract
OBJECTIVES: To study the prevalence of burnout among non-health care workers (NHCW), the risk and protective factors and to quantify the risk of burnout. METHOD: We conducted a cross-sectional study on the 3142 NHCW of the University Hospital of Clermont-Ferrand. They received a self-assessment questionnaire. RESULTS: Four hundred thirty seven (13.9%) NHCW completed the questionnaires. More than three quarter (75.4%) of NHCW was in burnout, with one in five (18.7%) having a severe burnout. Job demand was the main factor explaining the increase in exhaustion and overinvestment was the main factor explaining the increase in cynicism. Effort-reward imbalance (ERI) multiplied the risk of severe burnout by 11.2, job strain by 3.32 and isostrain by 3.74. CONCLUSION: NHCW from hospital staff are at high risk of burnout. The two major models of stress at work, the job demand-control-support and the ERI, were highly predictive of burnout, with strong dose-response relationships.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".