Physician Migration at its roots: Emigration Intentions and Preferences among Medical Students of a Nigerian University in the Niger Delta Region
Bibliographic record
Abstract
Background: Emigration of physicians from developing countries like Nigeria to industrialized countries has deprived the former of vital human health workforce. With the flight of doctors and the associated brain drain, poor economies and subsequent poor financing of the health sector, the health sector becomes overburdened with myriads of health issues. The study aimed to determine the emigration intentions and preferences of medical students, who are the future physicians. Method: This cross-sectional study was carried out among fourth - year medical students between August and October 2019. One hundred and thirty nine eligible students were enrolled. A semistructured questionnaire was used to collect the necessary data. Data was analyzed with SPSS software. Results: One hundred and three students completed the survey. Seventy respondents (68.0%) reported that they had intention to emigrate outside Nigeria. Only seventy one (68.9%) respondents believed that there were ample career opportunities in Nigeria. The preferred top destination countries were Canada and the United States of America. Lack of professional prospect (61.1% of responses) was the most common 'push factor' while opportunity to gain more experience (69.9%) and better working condition (49.5%) were the major 'pull factors. Emigration intention was negatively predicted by age and 'belief in career opportunities in home country' Conclusion: Most of the medical students in this study had intentions to emigrate aside their home country after graduation. There is a need for concerted efforts by the government, key stakeholders and individuals to stem the ugly tide of the medical brain drain. Keywords: Brain drain, Emigration, Medical students, Physicians, Nigeria
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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 | 0.002 |
| 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) | 0.002 | 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".