PUSK AND PULL FACTORS OF EMIGRATION AMONG PHYSICIAN IN NIGERIA.
Bibliographic record
Abstract
Background: Physician emigration is increasing exponentially in developing countries. In Nigeria, with the last decade's unprecedented brain drain, it has gained the popular moniker 'japa syndrome'. Aim: This study aimed to determine push and pull factors affecting physician migration in Nigeria, to provide evidence-backed recommendations for physician retention policies. Materials/Methods: A cross-sectional study was conducted among attendees at the 2022 Abuja Cardiovascular Symposium hosted by Limi Multispecialty Hospital and the Nigerian Cardiac Society. Convenience and snowball sampling were used, and 295/400 responded to comprehensive self-administered questionnaires (73.7% response rate). Data was analysed using SPSS v.26. Results: Most participants (79.4%) were aged 20-39 years (Mean 35 years SD ±10.17); female (58.6%); married (58.4%) and had family size below six (73.6%). About 85.8% were employed, and 55.9% worked in private establishments. Solely basic medical degrees were possessed by 64.4%, and 63.7% earned N300,000-N399,999 (USD 396.82-USD 527.78) monthly. Top destinations were UK (50.5%), Canada (43.3%), and USA (37.9%), with low remuneration (71.2%), insecurity (62.7%), and difficult working environments (55.9%) most frequent push factors. Postgraduate-training frustrations (38.6%), and limited educational opportunities for oneself (37.6%), children (26.4%), or spouse (19.7%) were the least. High earning potential (76.6%), career growth opportunities (70.8%), and high-level equipment/technology (54.9%) were frequent pull factors. Conclusion: Physician emigration threatens Nigeria's health system and should be addressed multi-sectorally to boost physician remuneration and improve work environments and societal security. Additionally, innovative education and digital technology would encourage health workforce retention.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".