Internationally educated nurses’ workforce and workplace integration experiences during the COVID-19 pandemic in Ontario: Qualitative descriptive study
Bibliographic record
Abstract
Background. Integrating internationally educated nurses (IENs) into Ontario’s healthcare workforce is crucial for addressing nurse shortages, meeting increased healthcare demands, and ensuring quality patient care. However, internationally educated nurses face numerous challenges during integration. Their experiences following the 2015 registration requirement changes and the COVID-19 pandemic remain unknown. This study aimed to understand and describe internationally educated nurses’ experiences during their integration processes and the support needed to streamline them. Methods. This study employed a qualitative description approach, using semi-structured one-on-one virtual interviews with twelve internationally educated nurses. Data collection and analysis were completed concurrently and informed by the Braun and Clark framework and the Transition Theory. Results. Three main themes with twelve subthemes emerged from internationally educated nurse interview analyses: internationally educated nurse experiences pre-registration, experiences post-registration, and support and call for improvements. Conclusion. This study highlighted the multifaceted challenges internationally educated nurses face when integrating into the Canadian healthcare system. Collaboration among all stakeholders, including internationally educated nurses, is essential to overcoming these challenges and facilitating 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".