Human Resources for Health Challenges in Nigeria and Nurse Migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emigration of sub-Saharan African health professionals to developed Western nations is an aspect of increasing global mobility. This article focuses on the human resources for health challenges in Nigeria and the emigration of nurses from Nigeria as the country faces mounting human resources for health challenges. Human resources for health issues in Nigeria contribute to poor population health in the country, alongside threats from terrorism, infectious disease outbreaks, and political corruption. Health inequities within Nigeria mirror the geographical disparities in human resources for health distribution and are worsened by the emigration of Nigerian nurses to developed countries such as the United States and the United Kingdom. Nigerian nurses are motivated to emigrate to work in healthier work environments, improve their economic prospects, and advance their careers. Like other migrant African nurses, they experience barriers to integration, including racism and discrimination, in receiving countries. We explore the factors and processes that shape this migration. Given the forces of globalization, source countries and destination countries must implement policies to more responsibly manage migration of nurses. This can be done by implementing measures to retain nurses, promote the return migration of expatriate nurses, and ensure the integration of migrant nurses upon arrival in destination countries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it