Downsizing and Organizational Restructuring: What Is the Impact on Hospital Performance?
Bibliographic record
Abstract
In recent years, hospitals have radically restructured their operations while significantly downsizing their workforces. To date, little is known about the combined effect of these change processes on organizational functioning. There have been few large‐scale studies investigating how hospitals have performed when both organizational restructuring and downsizing are used concurrently. The research reported here sets out to separate and isolate the independent and combined effect of organizational restructuring and downsizing on hospital performance. In particular, it aims to address the following question: Do hospitals which undergo significant organizational restructuring while maintaining their workforce complement perform any better than hospitals that institute significant restructuring while heavily downsizing, and any better than hospitals which heavily downsize but undertake little or no organizational restructuring? Categorical regression analysis results from a sample of 285 Canadian acute care hospitals suggest that organizational restructuring and downsizing have differential impacts on organizational performance. Hospitals which undertook significant organizational restructuring while heavily downsizing were perceived to perform better than hospitals that heavily downsized but conducted little or no organizational restructuring, but performed worse than hospitals that undertook significant restructuring while maintaining their workforce complement. However, when the method of conducting the change management process was controlled for, these performance differences were reduced or eliminated.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".