International Nurse Migration to Canada: Are We Missing the Bigger Picture?
Bibliographic record
Abstract
The report "Tested Solutions for Eliminating Canada's Registered Nursing Shortage" (Tomblin Murphy et al. 2009) projects a nursing shortage in Canada and offers strategies for eliminating that shortage. Using a population needs-based approach, the report estimates that in the year 2007, the shortage of nurses was 11,000 full-time equivalent (FTE) registered nurses. By 2022, the shortage will grow to almost 60,000 FTE RNs. Strategies proposed by the authors to address the RN shortage include increasing nurses' productivity, reducing annual absenteeism, increasing enrolment and reducing attrition in entry-to-practice education programs, improving the retention of practising RNs and reducing international in-migration of nurses.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".