Not Very Welcoming: A Survey of Internationally Educated Nurses Employed in Canada
Bibliographic record
Abstract
Abstract Background: Countries around the world are struggling to cope with a shortage of nurses and are increasingly relying on internationally educated nurses to fill the gap. Internationally educated nurses represent 9% of the Canadian nursing workforce, but this is expected to grow as the shortage continues. This study aimed to identify and understand the experiences of internationally educated nurses who came to Canada to seek nursing work. Methods: A cross-sectional survey of a random sample of internationally educated nurses was conducted. Descriptive statistics were used to analyze the survey responses. The survey also included an open-ended question about experience with the move to Canada to work as a nurse. Responses to the open-ended question were content analyzed and triangulated with the survey data. Results: A total of 2,107 internationally educated nurses responded to the study (47% response rate). Most were female (95%) and married (80%), and almost half were from the Philippines (49%). Professional (e.g., salary & benefits, 60%) and personal (e.g., quality of life, 56%) reasons drove migration to Canada, but 76% reported no recruitment incentives, and most (56%) relied on friends and family for information about nursing in Canada. Significant barriers to practicing in Canada included the licensure exam (75%), and obtaining information about different types of practice in Canada (56%). Conclusions: The findings from this study provide important information about internationally educated nurses’ perceptions and experiences of coming to Canada to obtain work in nursing. Improving the means for seeking employment by overseas nurses is a key area that regulatory agencies, health managers and policy leaders need to understand and address. Strategies to improve the barriers nurses face, particularly those related to licensure are important considerations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".