Is the grass any greener? Canada to United States of America nurse migration
Bibliographic record
Abstract
AIM: Little or no attempt has been made to determine why nurses leave Canada, remain outside of Canada, or under what circumstances might return to Canada. The purpose of this study was to gain an understanding of Canadian-educated registered nurses working in the USA. DATA SOURCES: Data for this study include the 1996, 2000 and 2004 USA National Sample Survey of Registered Nurses and reports from the same time period from the Canadian Institute for Health Information. FINDINGS: This research demonstrates that full-time work opportunities and the potential for ongoing education are key factors that contribute to the migration of Canadian nurses to the USA. In addition, Canada appears to be losing baccalaureate-prepared nurses to the USA. DISCUSSION: These findings underscore how health care policy decisions such as workforce retention strategies can have a direct influence on the nursing workforce. Policy emphasis should be on providing incentives for Canadian-educated nurses to stay in Canada, and obtain full-time work while continuing to develop professionally. CONCLUSION: Findings from this study provide policy leaders with important information regarding employment options of interest to migrating nurses. STUDY LIMITATIONS: This study describes and contrasts nurses in the data set, thus providing information on the context of nurse migration from Canada to the USA. Data utilized in this study are cross-sectional in nature, thus the opportunity to follow individual nurses over time was not possible.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".