The Influence of Organizational Factors on Registered Nurses’ Work Attitudes in Nigeria
Bibliographic record
Abstract
This study examined the influence of competence development, work-life balance, perceived organizational support and organization’s commitment to employees on job satisfaction, affective commitment and turnover intention among registered nurses in Nigeria’s Ondo State. The sample consisted of 220 registered nurses from six public hospitals in Ondo State. Data analysis was conducted using multivariate regressions, Pearson’s product-moment correlation and descriptive statistics to determine the influence of organizational factors on nurses’ job satisfaction, affective commitment and turnover intention. The results indicated that competence development practices, work-life balance policies and practices, perceived organizational support and the organization’s commitment to employees were positively correlated to job satisfaction and affective commitment but negatively correlated to registered nurses’ turnover intention. This study identified the importance of organizational factors in promoting nurses’ job satisfaction, affective organizational commitment and intention to stay which may inform hospital administration, health care institutions and the Ondo State Government about the significant role of organizational factors in improving nurses' job satisfaction, affective commitment and turnover intention.
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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 | 0.001 | 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".