Understanding the exodus: a 15-year retrospective cohort study on the pattern and determinants of migration among Nigerian doctors and dentists
Bibliographic record
Abstract
BACKGROUND: Nigeria faces a critical shortage of healthcare professionals yet experiences a significant annual exodus of doctors and dentists. This alarming trend threatens the country's ability to provide equitable healthcare. OBJECTIVE: This study investigated the patterns and determinants of migration among doctors and dentists who graduated from the University of Benin, Nigeria, 15 years ago. METHODS: We conducted a retrospective cohort study that tracked 274 of the 379 (72.3%) eligible cohort. We computed the migration incidence rate per person-year from 2008 to 2023, covering 3,455 person-years of follow-up and analysed migration drivers as push and pull factors across macro-, meso-, and micro-levels. RESULTS: Fifteen years post-graduation, 48.9% (134/274) of the cohort had migrated. While the annual incidence rate of migration remained stable for the first 8 years, it spiked after 2016, reaching 11.4 per 100 person-years in 2023. Among those who migrated, the majority (96.3%, 129/134) relocated outside the African continent. The top three destination countries were the UK (48.5%, 65/134), Canada (20.9%, 28/134), and the USA (19.4%, 26/134). The leading push factors were insecurity of lives and property (57.8%), concerns about children's futures (50.3%), and limited career development opportunities (45.9%). The primary pull factors included security (56.3%), permanent residency (49.6%), and better pay in the destination country (46.7%). Significant predictors of migration included younger age, timing of marriage, and residency training status. CONCLUSIONS: To avert an impending crisis, the Nigerian government must address the root causes driving the increasing migration of doctors and dentists.
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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 | 0.000 |
| 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".