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 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 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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".