Stakeholders’ perspectives on internationally educated nurse workforce and workplace integration pathways in Ontario: Qualitative descriptive study
Bibliographic record
Abstract
Background. Integrating internationally educated nurses into the Canadian healthcare system is a multifaceted process involving numerous stakeholders who can influence their successful integration. Since the registration examination changes in Canada, in 2015 and amidst evolving registration requirements, especially during the COVID-19 pandemic, little is known about stakeholder perspectives. This study aimed to describe stakeholder views on internationally educated nurse integration processes and the strategies to streamline these processes. Methods. This study employed a qualitative description approach, using semi-structured one-on-one virtual interviews with six stakeholders. Data collection and analysis were completed concurrently, guided by the Braun and Clark framework and the Fourfold Model of Acculturation Theory. Results. Two main themes emerged from data analysis: stakeholder insights during workforce and workplace integration. Stakeholders valued the internationally educated nurses’ expertise in Canadian healthcare and were committed to improving the licensing process to accelerate integration. Conclusion. This study highlights stakeholder perspectives on the integration pathways of IENs into the Canadian healthcare system. Collaboration among stakeholders, including IENs, is essential to streamline integration processes. cesses.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".