Stress and the effects of hospital restructuring in nurses.
Bibliographic record
Abstract
This study examines the extent of stress and burnout experienced by nurses during hospital restructuring. It includes both job-related outcomes such as job satisfaction and burnout, and psychosomatic outcomes such as depression. The study compares effects attributable to number of hospital restructuring initiatives with those attributable to specific work stressors such as workload, bumping (where one nurse replaces another due to greater seniority), and use of unlicensed personnel to do the work of nurses. It also examines the role of personal resources including self-efficacy and coping. Results show that, in hospitals undergoing restructuring, workload is the most significant and consistent predictor of distress in nurses, as manifested in lower job satisfaction, professional efficacy, and job security. Greater workload also contributed to depression, cynicism, and anxiety. The practice of bumping contributed to job insecurity, depression, and anxiety. The results point to specific deleterious effects of hospital restructuring. Implications of the findings are discussed. The extent to which workload issues are managed through appropriate practices can be expected to match the extent of nurses' experience of either job satisfaction or depression and anxiety. Such practices need to be part of an ongoing process of interaction between the hospital administration and nurses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".