Internationally Educated Nurse Integration: Insights From the Supervised Practice Experience Partnership Program at the University Health Network
Bibliographic record
Abstract
Background: Tailored and responsive programs are essential for integrating and retaining internationally educated nurses (IENs). The Supervised Practice Experience Partnership (SPEP) program in Ontario supports IENs in their transition. The University Health Network in Ontario developed a customized SPEP program to facilitate a two-way integration into practice, engaging IENs and practice leaders. Method: The advanced practice nurse educator (APNE) team leveraged continuous evaluation approaches to develop and refine the program, using a combination of surveys and focus groups with IENs and leaders (e.g., managers, APNEs and preceptors). Core components of the evidence-informed and adapted SPEP program include onboarding workshops, structured learning pathways, IEN and preceptor handbooks and a community of practice that incorporates peer support from successfully transitioned IENs. Findings: Evaluation results show improvements in IENs' critical thinking, communication, clinical reasoning and judgement. However, limited clinical experience among many IENs led to curricular adjustments emphasizing nursing fundamentals through case-based learning aligned with organizational values. Ongoing support from dedicated SPEP APNEs, particularly with prior experience as IENs, has been key to successful implementation. Conclusion: This data-informed, adaptive approach has enhanced IEN integration into the workforce and offers a practical model for nursing leaders and educators designing transition programs in dynamic healthcare settings.
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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 | 0.009 | 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 | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".