Factors Affecting Turnover Intention among Nurses in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Reducing turnover is essential to address health worker shortages in the public sector and improve the quality of services. This study examines factors associated with Ethiopian nurses' intention to leave their jobs. METHODS: Survey respondents (a sample of 425 nurses at 122 facilities) rated the importance of 20 items in decisions to leave their jobs and reported whether they intended to leave their jobs in the next year. Descriptive and inferential statistical analyses were used to identify predictors of nurses' intentions to leave their jobs. RESULTS: Half (50.2%) the nurses said they intended to leave their jobs in the next year. A multivariate analysis identified three significant predictors of nurses' intention to leave their jobs: holding a university degree rather than a diploma (adjusted odds ratio (OR)=2.246, 95% confidence interval (CI)=1.212, 4.163; p<0.01), having worked fewer years in the public health system (adjusted OR=0.948, 95% CI=0.914, 0.982; p<0.01) and rating the importance of limited opportunities for professional development more highly (adjusted OR=1.398, 95% CI=1.056, 1.850; p<0.02). CONCLUSION: Interventions to increase the retention of nurses at public health facilities in Ethiopia should target young nurses who are completing their compulsory service obligation and nurses with a university degree. They should include both non-financial and financial incentives.
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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".