Internationally educated nurses in Canada: predictors of workforce integration
Bibliographic record
Abstract
BACKGROUND: Global trends in migration accompanied with recent changes to the immigrant selection process may have influenced the demographic and human capital characteristics of internationally educated nurses (IENs) in Canada and in turn the assistance required to facilitate their workforce integration. This study aimed to describe the demographic and human capital profile of IENs in Canada, to explore recent changes to the profile, and to identify predictors of IENs' workforce integration. METHODS: A cross-sectional, descriptive, correlational survey design was used. Eligible IENs were immigrants, registered and employed as regulated nurses in Canada. Data were collected in 2014 via online and paper questionnaires. Descriptive statistics were used to examine the data by year of immigration. Logistic regression modeling was employed to identify predictors of IENs' workforce integration measured as passing the licensure exam to acquire professional recertification and securing employment. RESULTS: The sample consisted of 2280 IENs, representative of all Canadian provincial jurisdictions. Since changes to the immigrant selection process in 2002, the IEN population in Canada has become more racially diverse with greater numbers emigrating from developing countries. Recent arrivals (after 2002) had high levels of human capital (knowledge, professional experience, language proficiency). Some, but not all, benefited from the formal and informal assistance available to facilitate their workforce integration. Professional experience and help studying significantly predicted if IENs passed the licensure exam on their first attempt. Bridging program participation and assistance from social networks in Canada were significant predictors if IENs had difficulty securing employment. CONCLUSIONS: Nurses will continue to migrate from a wide variety of countries throughout the world that have dissimilar nursing education and health systems. Thus, IENs are not a homogenous group, and a "one size fits all" model may not be effective for facilitating their professional recertification and employment in the destination country. Canada, as well as other countries, could consider using a case management approach to develop and tailor education and forms of assistance to meet the individual needs of IENs. Using technology to reach IENs who have not yet immigrated or have settled outside of urban centers are other potential strategies that may facilitate their timely entrance into the destination countries' nursing workforce.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".