Willingness of Medical Students to Practice in their Country of Origin after Studies: A Nigerian Perspective
Bibliographic record
Abstract
Background: The availability of skilled manpower at service locations is an important indicator of the strength of the healthcare system and is critical for effective healthcare service delivery in developing countries. The emigration of doctors reported in Africa over the years has tremendously increased in recent times. The health sector in this low-income region has registered a great setback in their health indices following a severe shortage of manpower. Objective: This study was undertaken to assess the willingness of medical students to practice in Nigeria after the completion of their medical education. Methods: This was a cross-sectional study performed among medical students in Enugu State University Teaching Hospital, Parklane, Enugu, Nigeria. A pretested self-administered questionnaire was used for data collection. Information was analyzed using the Statistical Package for Social Sciences version 22 software. Descriptive statistics were used to summarize and present data. The degree of bivariate associations was measured using the Pearson Chi-Square test at a significance level of p <0.05. Results: The mean age of the respondents was 23.9 ± 3.4 years.The majority were males (58.0%) and a greater proportion of the respondents (83.5%) did not desire to practice in Nigeria after their studies with the USA (29.3%) and Canada (17.8%), being the most preferred countries of migration. Advancement in technology and better remuneration were the most compelling factors for emigration. Conclusion: To ensure adequacy and efficiency in the health sector as recommended by the World Health Organization, governments of low-income countries should put measures in place to make medical practice in their countries more attractive to young doctors. Such measures include improved remuneration for services rendered and incorporation of more modern technology into the health care delivery system.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".