Relationship between Job Satisfaction, Pay, Affective Commitment and Turnover Intention among Registered Nurses in Nigeria
Bibliographic record
Abstract
This study aims to examine the relationship between job satisfaction, pay, affective commitment, and turnover intentions of public hospitals-based Registered Nurses in Ondo State, Nigeria. Using the quantitative, cross-sectional survey design, data from 220 Registered Nurses were analysed. Results indicate that pay and job satisfaction have significant positive relationship with nurses’ affective commitment; pay has significant positive relationship with their job satisfaction but pay, job satisfaction and affective commitment have negative relationship with turnover intentions. Job satisfaction is of critical importance in gaining nurses’ affective commitment and enhancing retention. Pay is often considered as a hygiene factor in theories of motivation – meaning, even though pay decreases might cause dissatisfaction, pay increases would not increase satisfaction. This does not appear to be the case in Nigeria. These findings have implications for health human resource management in general and the management of nursing staff in the public hospitals of Ondo State, Nigeria in particular.
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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 | 0.000 | 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".