Driving factors of brain drain among medical students of the university of abuja college of health sciences, nigeria
Bibliographic record
Abstract
Brain drain which is the large-scale migration of highly trained professionals from low- and middle-income countries to wealthier nations, has emerged as a critical challenge to healthcare workforce stability in sub-Saharan Africa. Nigeria faces a particularly acute crisis, with medical students increasingly expressing the intention to migrate. To identify the key driving factors influencing the intention of medical students at the University of Abuja College of Health Sciences to emigrate after graduation. A cross-sectional descriptive study was conducted among 236 medical students selected via stratified random sampling. Data were collected using a structured, interviewer-administered questionnaire capturing socio-demographic characteristics, migration intentions, timing of intended departure, preferred destinations, and reasons for leaving or staying. Analysis was performed using SPSS version 26, with results presented in frequencies and percentages. A total of 73.7% of respondents expressed an intention to leave Nigeria after graduation, with 31.4% planning to do so within two years post-graduation. The most cited reason for migration was the pursuit of a safer and better working environment (30.1%), followed by higher salaries abroad (20.3%). The United Kingdom (19.5%), USA (16.9%), and Canada (16.5%) were the most preferred destinations. The migration intentions of medical students in Nigeria are driven primarily by poor working conditions, inadequate remuneration, and personal safety concerns. Urgent policy measures, including improved working environments, competitive renumerations, and strategic retention incentives, are required to avert further loss of the country’s future healthcare workforce.
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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) | 0.002 | 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".