Does Being a Visible Minority Matter? Predictors of Internationally Educated Nurses’ Workplace Integration
Bibliographic record
Abstract
STUDY BACKGROUND: Nurses continue to migrate to Canada. Majority are visible minorities. Once employed, internationally educated nurses can struggle to integrate into their workplaces. A comprehensive understanding of factors that support internationally educated nurses' workplace integration is lacking, limiting our ability to design appropriate policies and practices. PURPOSE: The aim is to (1) examine internationally educated nurses' perceptions of the extent to which they have integrated in their workplaces and the individual and contextual factors that supported their workplace integration, (2) explore whether internationally educated nurses' perceptions differed by visible minority status, and (3) identify the key factors that predict internationally educated nurses' workplace integration. METHODS: Cross-sectional survey of 1215 internationally educated nurses. All were immigrants, permanent residents, and employed as regulated nurses. Multiple linear regression was used to examine the influence of individual and contextual factors on perceived degree of workplace integration. RESULTS: .003), had statistically significant, positive associations with workplace integration. CONCLUSIONS: Internationally educated nurses' visible minority status can influence their workplaces. Providing education, managerial support, and mentorship fosters internationally educated nurses' workplace integration.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".