The Internationally Educated Nurses’ (IENs’) Integration Experiences During the COVID-19 Pandemic in Ontario: A Multi-Method Study
Bibliographic record
Abstract
Background. Integrating internationally educated nurses (IENs) into Ontario’s healthcare workforce is crucial for addressing nurse shortages, meeting high demands, and ensuring quality patient care. However, IENs encounter significant challenges in integrating into the Canadian healthcare system. Limited research exists on their experiences following the 2015 licensing requirement changes and during the pandemic. This study aimed to map IEN integration pathways in Canada, explore sociodemographic factors associated with success, and describe their integration experiences and support needs in Ontario. Methods. A multi-method study explored IEN integration experiences in Ontario. This study contains three phases: a) scoping review, b) quantitative analysis of primary and secondary data, and c) qualitative description studies using semi-structured interviews with twelve IENs and six stakeholders. Data collection and analysis were guided by the Braun and Clarke framework, Transition Theory, and the Fourfold Model of Acculturation Theory. Results. Collectively the findings from all phases intersected highlighting key points affecting IEN integration into the Canadian healthcare system. The scoping review of 27 studies revealed similarities between IEN integration pathways in Canada and Australia, with Australia being more successful. The key recommendation is to better align Canadian policies for improved integration. The quantitative analysis found that successful outcomes were associated with completing registration within the safe practice period and affiliation with an IEN initiative in Ontario. The qualitative analyses from IEN and stakeholder interviews identified interrelated themes. Three main themes from IENs: IEN experiences pre-registration, experiences post-registration, and support and call for improvements and stakeholder themes included insights on the IEN workforce and workplace integration pathways. Conclusion. This thesis highlights the complex challenges IENs face when integrating into the Canadian healthcare system. Stakeholders valued IENs’ expertise and demonstrated a commitment to improving the licensing processes to accelerate their integration. Findings from this study thesis emphasize the need for collaboration among stakeholders, including IENs, to overcome challenges of licensure and facilitate smoother integration processes for IENs.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".