Mitigating Nurse Emigration: Strategies for Retaining South Africa's Healthcare Workforce
Bibliographic record
Abstract
Purpose: This study aims to investigate the factors influencing the emigration of registered nurses from South Africa, focusing on their perceptions, motivations, and potential strategies for retention by employers and the government. Method: A quantitative, exploratory, and descriptive approach was employed, utilizing a structured questionnaire distributed to nursing students enrolled in distance education programs. The survey collected demographic data and insights into the reasons for emigration and retention strategies, with data analyzed using SPSS software. Findings: The primary motivations for nurse emigration were higher salaries, better working conditions, and career advancement opportunities abroad. The study revealed significant demographic influences, such as age, marital status, and family responsibilities, on nurses' decisions to emigrate. A high level of awareness regarding the emigration of peers and detailed understanding of emigration drivers were also noted. Novelty: This research provides a comprehensive analysis of nurse emigration from South Africa, highlighting the interplay between financial incentives, working conditions, and personal factors. It offers valuable insights for policymakers and healthcare administrators seeking to develop effective retention strategies. Conclusion: The study underscores the critical need for policies addressing income disparities, improving working conditions, and offering career development opportunities within South Africa to mitigate nurse emigration. These strategies are essential to maintaining a stable and effective healthcare workforce in the country
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".