Healthcare Restructuring: The Impact of Job Change
Bibliographic record
Abstract
Restructuring, particularly redeployment and job change, had a dramatic impact on the working conditions and practices of nursing personnel. This study was conducted to determine whether nurses (RNs and RPNs) who experienced job change perceived their work-lives differently than those who did not undergo job change and, whether nurses who experienced different types of job change (new role, new unit, or new hospital) varied in their perceptions. A questionnaire exploring themes relevant to redeployment was administered to all nurses (N = 3,408) in two large teaching hospitals that had undergone restructuring. The response rate was 50.7% (n = 1,728). Of the responses, 1,662 were used in the analysis. T-tests and ANOVAs were used to compare groups of nurses. Nurses who changed their jobs perceived their commitment to the organization, their work environment and quality of care differently than those who did not change jobs. Nurses with different types of job change differed in their organizational commitment, perceptions of work-related injuries, attitudes towards job change, need for orientation and new knowledge, and feelings about the health care team. Results will assist managers to address the specific needs of nurses with different experiences of job change in the restructured workplace.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".