Bibliographic record
Abstract
OBJECTIVE: To synthesize information about nurse migration in and out of Canada and analyze its role as a policy lever to address the Canadian nursing shortage. PRINCIPAL FINDINGS: Canada is both a source and a destination country for international nurse migration with an estimated net loss of nurses. The United States is the major beneficiary of Canadian nurse emigration resulting from the reduction of full-time jobs for nurses in Canada due to health system reforms. Canada faces a significant projected shortage of nurses that is too large to be ameliorated by ethical international nurse recruitment and immigration. CONCLUSIONS: The current and projected shortage of nurses in Canada is a product of health care cost containment policies that failed to take into account long-term consequences for nurse workforce adequacy. An aging nurse workforce, exacerbated by layoffs of younger nurses with less seniority, and increasing demand for nurses contribute to a projection of nurse shortage that is too great to be solved ethically through international nurse recruitment. National policies to increase domestic nurse production and retention are recommended in addition to international collaboration among developed countries to move toward greater national nurse workforce self sufficiency.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.022 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".