<b>Equity-Oriented Mentorship for Internationally Educated Nurses: A Rapid Review </b>
Bibliographic record
Abstract
Introduction:Internationally educated nurses (IENs) represent a critical proportion of the global nursing workforce and are central to addressing current and projected shortages. While mentorship is widely recognized as a facilitator of workforce integration, much of the existing literature has treated it as symbolic support rather than as equity-oriented infrastructure. This review examines how mentorship for IENs has been designed, implemented, and evaluated, with particular attention to equity, diversity, and inclusion (EDI) considerations. Methods:We conducted a rapid review following Joanna Briggs Institute guidance and reported according to PRISMA standards. Searches of peer-reviewed and grey literature from 2010 to 2025 identified 51 relevant sources, including empirical studies, program evaluations, and policy reports. Data were extracted and synthesized thematically, with attention to micro-level (identity and lived experience), meso-level (organizational and regulatory structures), and contextual (mentorship models and design) factors. Results:Findings demonstrate that while mentorship is consistently valued by IENs and employers, most programs lack sustained equity-oriented design features. Effective initiatives included culturally congruent mentor–mentee pairing, structured orientation supports, protected time for mentors, and organizational accountability mechanisms. Programs situated within multi-stakeholder partnerships and supported by explicit equity frameworks were associated with stronger integration outcomes, including increased retention, reduced professional isolation, and enhanced career satisfaction. However, mentorship was often undermined by systemic barriers such as licensure delays, underemployment, and racialized power dynamics. Conclusions:Mentorship has significant potential to serve as an equity-oriented strategy for IEN workforce integration and retention. For mentorship to move beyond symbolic support, programs must be embedded in organizational policy, resourced adequately, and evaluated against equity-focused outcomes. This review contributes actionable principles for designing and sustaining mentorship that recognizes the intersectional realities of IENs, advancing both workforce stability and justice in nursing.
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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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".