Global Nurse Migration: Its Impact on Developing Countries and Prospects for the Future
Bibliographic record
Abstract
The worldwide shortage of nurses which results from a global undersupply and high attrition rates affects developed countries in the West the same way as it affects developing countries in Asia, Africa and Latin America. The difference lies in the fact that developing countries serve as a readily available source of trained nurses for developed countries in Europe, North America and parts of Oceania. Strong "pull" and "push" factors favour wealthier nations in the West in their efforts to deal with domestic shortages through overseas recruitment. Thus, the ongoing nursing shortage in developing countries is worsened by a loss of thousands of trained nurses every year to emigration. This paper brings into focus the magnitude of the problem in terms of the number of nurses migrating to and from various countries and its impact on developing countries. The paper also examines some of the ongoing efforts in developing countries to mitigate the problem and sheds light on the prospects for improvement in the foreseeable future.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".