Human Resources for Health Challenges in Nigeria and Nurse Migration
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".