Exploring the emigration intentions of Nigerian medical and nursing students: factors driving migration and implications for Nigeria’s healthcare system
Bibliographic record
Abstract
BACKGROUND: The emigration of healthcare professionals significantly contributes to brain drain within Nigeria's healthcare sector, exacerbating existing workforce shortages. This study investigates the emigration intentions of Nigerian medical and nursing students, focusing on preferred destinations, key motivating factors, and the potential long-term consequences for the nation's healthcare system. METHODS: A cross-sectional study was conducted among undergraduate medical and nursing students from six universities, purposefully selected to represent Nigeria's geopolitical zones. A total of 2,152 students (Medicine and Surgery = 1254; Nursing = 898) participated in the study. Data were collected using a structured, self-administered online questionnaire and analysed with IBM SPSS version 27. Descriptive statistics, chi-square tests, and binary logistic regression were applied, with statistical significance set at p < 0.05. RESULTS: 72.9% of students expressed intentions to practice abroad, primarily seeking specialist training within the first five years post-graduation (97.7%). Alarmingly, 32.7% had no intention of ever returning to Nigeria, while only 11.7% of those intending to stay intend to leave after completing specialist training. The top three emigration destinations were the United States (28.5%), the United Kingdom (24.6%), and Canada (23.1%). The main drivers of emigration included better training opportunities (75.2%), access to advanced equipment (61.1%), and improved career prospects (56.7%). Respondents predicted negative impacts on Nigeria's healthcare system, including increased mortality rates and potential system collapse. CONCLUSION: The findings reveal a high propensity for emigration among Nigerian medical and nursing students, with significant implications for the country's healthcare system. The study underscores the urgent need for policy interventions that address systemic challenges such as inadequate resources, poor working conditions, remuneration and career development opportunities. Strengthening local training conditions and offering competitive incentives may help mitigate the brain drain and ensure a sustainable healthcare workforce in Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".