Relationship between perceived organizational politics, organizational trust, human resource management practices and turnover intention among Nigerian nurses
Bibliographic record
Abstract
Prior research has indicated that employee turnover is detrimental to both individuals and organisations. Because a turnover intention in the workplace is detrimental, several factors have been suggested to better understand the reasons why employees may decide to leave their organisations. Some of the organizational-related factors that have been considered by previous research include perceived organizational justice, job satisfaction, perceived psychological contract breach, and perceived organizational support, among others. Despite these empirical studies, literatures indicate that less attention has been paid to the influence of perceived organisational politics, organizational trust, and perceived human resource practices management (HRM) practices on employee turnover. Hence, the present study fills in the gap by examining the relationship between perceived organisational politics, organizational trust, perceived human resource management practices and employee turnover among Registered Nurses in Nigerian public hospitals using multiple regression analysis technique. One hundred and seventy five Registered Nurses participated in the study. Result indicated that perceived organisational politics was significantly and positively related to turnover intentions. The result also showed that both organizational trust and perceived human resource practices were significantly and negatively related to turnover intentions. Theoretical and practical implications of the results are discussed.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".