Incentivizing an exodus: The implications of recruiting nurses from low-middle income countries to high-income countries
Bibliographic record
Abstract
In recent months, to palliate against a shortage of nurses, several high-income countries (HICs) have turned to low and middle-income countries (LMICs) to recruit nurses to their healthcare systems, despite the global nursing shortage disproportionately affecting LMICs [1].This approach is ill-conceived.High-income countries do not have a shortage of registered nurses (RNs); they have a shortage of healthcare institutions providing necessary and sustainable working conditions, leading to a loss of nurses [2].In Canada, the number of vacant RN positions increased from 10,400 to 22,400 (85.8%) from 2019-2021, despite a net growth of 7910 nurses (+2.5%) from 2020-2021 [3].Rather than resorting to recruiting nurses overseas, governments should implement other solutions with documented success: safe nurse-patient ratios and measures to protect nurses from structural and workplace violence.
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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 | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".