Bibliographic record
Abstract
The paper "Global Nurse Migration: Its Impact on Developing Countries and Prospects for the Future" by Khaliq, Broyles and Mwachofi explores the global nurse migration experience in developed and developing countries. Nurses choose to migrate from developing countries in response to economic and political instability, and poor salaries and working conditions in their home countries. These factors also interfere with the home countries' capacity to deliver education for health professions and to provide adequate support for a healthcare system. The paper clearly outlines the catastrophic outcomes of nurse migration for developing countries where inadequate numbers of trained and experienced nurses remain to support education and administration. Many well-documented references in the paper report overburdening of the remaining workforce, reduced access to care, increased risk of patient mortality, increased maternal, infant and under-five-year mortality rates, reduced life expectancy, increased incidence of communicable diseases and reduced immunization, all resulting from shortages of health professionals, leading to increased nursing workload and burnout.
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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.087 | 0.075 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".