A Conceptual Model to Enhance Employer Readiness: An Adaptive Leadership Approach to the Integration of Internationally Educated Nurses
Bibliographic record
Abstract
Canada’s healthcare system is navigating a significant nursing workforce crisis. In response, Alberta Health Services, the largest healthcare authority in the province of Alberta, launched the Recruitment, Transition, and Integration of Internationally Educated Nurses (RTIEN) initiative to recruit 1,000 IENs. Although this plan addresses immediate workforce shortages, it exposes deeper systemic gaps in employer readiness, integration infrastructure, and equity-informed support practices. This Doctor of Nursing project introduces the conceptual model to enhance employer readiness for IEN integration (CMEERI), a field-based, evidence-informed framework designed to guide structured, sustainable, and inclusive IEN integration across diverse healthcare settings. Grounded in the EPIS Implementation Science Framework, adaptive leadership theory, and knowledge translation principles, CMEERI repositions IEN integration as a relational and systemic transformation rather than a technical or transactional process. Findings revealed that system readiness, leadership capacity, and relational infrastructure, such as psychological safety, DEI supports, and mentorship, were critical to integration success. Sites with stronger leadership engagement and team-based learning cultures demonstrated more inclusive and sustained outcomes. By positioning readiness as a strategy and integration as a leadership responsibility, CMEERI offers a scalable pathway to strengthen workforce capacity, advance equity, and support long-term health workforce transformation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".