Multiorganizational Partnerships: A Mechanism for Increasing the Employment of Internationally Educated Nurses
Bibliographic record
Abstract
BACKGROUND: Internationally educated nurses (IENs) face multiple challenges in entering and integrating into the Canadian workforce. These challenges include getting to know the Canadian culture, nursing accountabilities, professional practice requirements and experience or qualifications deemed not equivalent to the Canadian standard. Hamilton Health Sciences' (HHS') IEN Integration Project has been funded by the Ontario and Canadian governments to support IENs in overcoming these challenges and contribute to the healthcare system. AIM: The aim of this article is to describe a multiorganizational project that prepares IENs for employment in Canadian healthcare. STRATEGY: HHS invited partners in education and immigrant support services to co-design the project. A community collaboration employment model (CCEM) was developed to leverage each partner's strengths in targeted interventions to address the needs of IENs, as identified in focus groups. The interventions pertain to professional practice and accountability in the Canadian healthcare setting, workplace language, communication and selected clinical skills. RESULTS: Between project initiation in 2009 and early 2021, 591 IENs obtained employment. CONCLUSION: Multiorganizational partnerships can help build and sustain a strong nursing workforce, and IENs can fill gaps in care. A needs-based approach and the CCEM increased the likelihood of IEN employment. The ability of the CCEM to engage partners makes it relevant for healthcare organizations.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".