Organizational Justice and the Shortage of Nurses in Medical & Educational Hospitals, in Urmia-2014
Bibliographic record
Abstract
OBJECTIVE: One of the most important reasons of turnover is perceptions of organizational justice. The purpose of this study was to investigate the effect of perceived organizational justice and its components on turnover intentions of nurses in hospitals of Urmia University of Medical Sciences. METHODS: This cross-sectional study was among nurses. 310 samples were estimated according to Morgan Table. Two valid and reliable questionnaires of turnover and organizational justice were used. Data analysis was performed using the software SPSS20. Using the Kolmogorov-Smirnov test, the normality and relationship between variables with Pearson and Spearman correlation test were analyzed. RESULTS: Most people were married and aged between 26 and 35 years, BA and were hired with contraction. The mean score of organizational justice variable was 2.59. The highest average was the interactional justice variable (2.81) and then Procedural fairness variable (2.75) and distributive justices (2.03) were, respectively. The mean range of turnover variable was 3.10. The results showed weak and negative relationship between various dimensions of organizational justice and turnover in nurses. CONCLUSION: Organizational justice and turnover had inverse relationship with each other. Therefore how much organizational justice in the organization is more; employees tend to stay more. Finally, suggestions for improvement of justice proposed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".