Integration trends of internationally educated nurses in Canada and Australia: A scoping review
Bibliographic record
Abstract
Background: Canada is less successful in integrating internationally educated nurses (IENs) into the nursing workforce than other developed countries like Australia. Objective & Design: This scoping review compares the integration trends of internationally educated nurses in Canada with those in Australia. Data Sources & Methods: Nine online databases were searched for English-language studies on the integration pathways of IENs in Canada or Australia. Results: Twenty-seven articles included in the review were completed in Canada (62.96%, n=17) and Australia (37%, n=10). The articles reported on internationally educated nurse integration in the workforce (18.5%, n=5) and workplace (66.7%, n=18), and four studies (14.8%) examined both workforce and workplace integration. This review highlighted the difficulties IENs encounter during their integration in Canada and Australia. Collaboration among stakeholders in Australia resulted in better integration of IENs. Conclusion: This review suggests Canadian ministries collaborate and align their policies to support better integration of internationally educated nurses into the Canadian health system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".