Hospital restructuring and downsizing: Effects on nursing staff well-being and perceived hospital functioning
Bibliographic record
Abstract
The health care system, and hospitals, underwent considerable restructuring and downsizing in the early to mid-1900s in several countries as governments cut costs to reduce their budget deficits. Studies of the effects of these efforts on nursing staff and hospital functioning in various countries generally reported negative impacts. Health care restructuring and hospital downsizing was again being implemented in North America in 2009/2010 as governments struggled to once again reduce deficits at a time of worldwide economic recession. This study examines the relationship of downsizing and restructuring efforts with work and well-being outcomes. Data were collected from over 289 nursing staff working in California hospitals in 2009/2010. This research considers the relationship of number of hospital restructuring initiatives reported by nursing staff with indicators of their work satisfaction and psychological well-being and their perceptions of the impact of these initiatives on aspects of hospital performance. Nurses reported a relatively large number of restructuring and downsizing initiatives during the preceding year. Consistent with findings reported over 15 years ago, nursing staff reporting a greater number of restructuring and downsizing initiatives indicated less favorable work and well-being outcomes and more negative effects on hospital functioning. Some suggestions for more successful approaches to cost reductions are offered.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".