Reducing Barriers and Achieving Success in Registration Examination Among Internationally Educated Nurses: A Participatory Action Research Project
Bibliographic record
Abstract
Background Multiple challenges impede the Internationally Educated Nurses (IENs) professional development and success in writing the registration examination. This paper aims to explore these challenges and describes the educational program which adopts a tailored mentoring approach to facilitate their successful completion of the registration exam. Methodology: Participatory Action Research model informed the development and revision of the educational process. For this qualitative study, individual 1:1 audiotaped and telephone interviews were conducted among the initial participants to explore their experiences in the program. Results Findings from this study provided more insights as to participants’ success in passing registration examination and enhanced performance in their clinical practices. From the thematic analysis, we interpreted the IEN’s journey in their knowledge transition into four major themes: Acknowledging the barriers, Learning the new culture, Making progress, Transitioning into power. Conclusion This project demonstrated that the integration of a strong mentor-mentee relationship that gives voice to the participants’ learning experience so as to meet their knowledge gaps, engenders a deeper understanding of Canadian professional nursing practice, positions them for success in writing the registration examination and builds a sense of empowerment among them.
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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 | 0.082 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".