Internationally educated nurses in Canada: perceived benefits of bridging programme participation
Why this work is in the frame
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Bibliographic record
Abstract
AIM: To examine internationally educated nurses' perceptions of the extent to which participating in bridging programmes is beneficial for preparing to practise nursing in Canada. BACKGROUND: Internationally educated nurses continue to migrate from low-income to high-income countries. Many experience challenges when attempting to practise their profession in the destination country. Canada and other top destination countries offer educational support, such as bridging programmes, to assist internationally educated nurses' with preparing to practise nursing in the destination country. The research evidence falls short in demonstrating the usefulness of bridging programmes. METHODS: A subsample of 360 internationally educated nurse participants from a Canadian cross-sectional survey conducted in 2014. All were permanent residents, employed as regulated nurses and participants of bridging programmes. Multiple linear regression was employed to examine the influence of internationally educated nurses' human capital (academic preparation, language proficiency, professional experience) and the economic status of their source country on perceived benefits of bridging programme participation. RESULTS: Regression model explained 11.5% of variance in perceived benefits of bridging programme participation. Two predictors were statistically significant: source country and professional experience. CONCLUSION: Bridging programmes help internationally educated nurses address gaps in their cultural, practical and theoretical knowledge. Source country and amount of professionally experience influences the extent to which internationally educated nurses benefit from participating in bridging programmes in Canada. IMPLICATIONS FOR NURSING POLICY: Provides emerging evidence for decision-makers globally when developing policies and supports to help internationally educated nurses integrate into the destination country's nursing workforce.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it