Effects of Hospital Restructuring and Downsizing on Nursing Staff
Bibliographic record
Abstract
The healthcare system underwent considerable restructuring and downsizing in the early to mid-1990s as governments cut costs to reduce their budget deficits. Studies of the effects of these efforts on nursing staff and hospital functioning generally reported negative impacts. Healthcare restructuring and hospital downsizing was again being implemented as governments struggled to once again reduce deficits. The present study examines the relationship of union support during hospital restructuring initiatives with a range of individual and unit/hospital outcomes in a sample of nursing staff working in healthcare settings (hospitals) undergoing significant restructuring and downsizing. Data were collected from 289 nursing staff in California hospitals. Nurses reported a relatively large number of restructuring and downsizing initiatives during the preceding year. Levels of union support had a significant relationship with hospital functioning, but not with nursing staff work and well-being outcomes. Although union support has not shown many benefits for nursing staff during hospital restructuring and downsizing given their focus on adherence to collective agreements, nursing unions can play a larger role here. The present study adds to our understanding of the potential benefits of union support during the current hospital restructuring and downsizing, and highlights the role of union leadership and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".