Application of an Impact of Restructuring Scale to the Healthcare Sphere
Bibliographic record
Abstract
Job loss and job insecurity are frequently associated with today's widespread restructuring and downsizing of the workplace. The authors have developed an Impact of Restructuring Scale that quantifies the effects of restructuring in the healthcare sphere. The scale documents the effects of the resulting cutbacks, hospita mergers and hospital closings on two areas: quality of healthcare services and effects on staff. The study described in this article applies the scale to the healthcare sphere as reported in a sample of 1,363 nurses employed in hospitals that were being restructured. The nurses returned a self-report questionnaire in which they reported their reactions on a variety of measures designed to assess extent of restructuring initiatives, stressors, hospital support, job satisfaction and distress. Results showed that predictors of the impact of restructuring on nurses include restructuring initiatives undertaken by the hospital, deterioration of hospital facilities and services, work stressors (e.g., workload, bumping, use of generic workers) and social support. Hospital restructuring was also associated with lower job security, diminished job satisfaction and increase in depression, anxiety and somatization.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".