Influence of Socio-Cognitive Factors on Intention to Emigrate among Nurses Working in Nakuru County, Kenya
Bibliographic record
Abstract
Nurse emigration is the process by which nurses move from their home country to another country in search of better job opportunities, higher salaries, improved working conditions, and international experience. In Kenya, nurse emigration is a growing challenge often attributed to deficits in Human Resource Standards. This high level of emigration imposes additional costs on the government and taxpayers due to repeated training cycles and disruptions in the delivery of quality healthcare services. Beyond HR-related factors, demographic, psychological, and psychosocial dimensions also play a critical role. Understanding these factors in conjunction with HR standards is essential for improving healthcare delivery and workforce stability. This study investigated nurses’ intention to migrate within the context of Nakuru County, Kenya. It involved a sample of 150 nurses employed across seventeen health facilities in the Nakuru West Sub-County. Guided by Social Cognitive theory, the study adopted a cross-sectional design to explore the effects of socio-cognitive factors on emigration intention. Results revealed that 80.7% (n=150) of nurses firmly intend to emigrate, with 74.7%(n=150) already enrolled in immigration processes. The United States was the most preferred destination, followed by the United Kingdom (18.7%, n=150), Australia (10.7%, n=150), and Canada (4.7%, n=150). Socio-cognitive factors significantly influenced emigration intention: motivation (β = 0.353, p < .05), attention (β = 0.346, p < .05), and self-efficacy (β = 0.348, p < .05). These variables explained a substantial portion of the variance in emigration intention, underscoring their predictive strength. These findings highlight the urgent need to address cognitive determinants of nurse emigration to reduce emigration 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".