Brain drain in medically challenged context: A study of the push, pull, and stick factors among a population of medical practitioners in Nigeria
Bibliographic record
Abstract
Background: Globally, brain drain (BD) phenomenon has been an issue for decades in healthcare industry. However, the magnitude of BD syndrome and its impact on medical workforce crisis in a medically challenged environment has been the subject of great interest in the recent years, with apparently glaring effects on the medical workforce. Aim: The study was aimed at describing the push, pull, and stick factors, benefits, and preventive measures for BD among medical practitioners in Abia State, Nigeria. Subjects and Methods: This was a cross-sectional study carried out on 185 medical practitioners in Abia State, Southeastern Nigeria. Data collection was done using pretested, self-administered, and structured questionnaire that elicited information on push, pull, and stick factors, benefits, and preventive measures for BD. The plan to leave Nigeria and preferred foreign countries were also studied. Results: The age of the participants ranged from 26 to 72 (36 standard deviation 8.4) years. There were 159 (85.9%) males. One hundred and twenty-seven, 127/185 (68.6%) study participants had plans to leave the country with the most preferred countries of destination being Canada, United States, United Kingdom and Australia. The most common push factors from Nigeria and pull factors from abroad were similar and included poor income, wages, and salaries in all the participants 185/185 (100%). The most predominant stick factor was family-centric reasons, 126/185 (68.1%). Family and national family remittances were the main benefits, 185/185 (100%) for each while the most common pull factor was higher income, wages, and salaries abroad, 185/185 (100%). The most predominant stick factor was family-centric reasons, 126/185 (68.1%). The greatest benefits were family, 185/185 (100%), and national, 185/185 (100%), financial remittances. The most recommended preventive measures were enhanced income in Nigeria, 185/185 (100%). Young adult age ( P < 0.001), male ( P < 001), and duration of practice <10 years ( P < 0.001) were significantly associated with the plan to leave the country. Conclusion: These findings demonstrates that about 70% of Nigerian medical practitioners plan to leave the country for abroad. The major underlying factors for brain drain include enhanced income in the destination country capacity for financial remittances to the family and nation.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".