Internationally Educated Nurses’ and Their Contributions to the Patient Experience
Bibliographic record
Abstract
Internationally educated nurses (IEN) are a group who reflect Canada’s diverse population as a result of rising immigration trends. There is increasing diversity of the general population in Canada and health service disparities exist. Reducing these disparities among the healthcare workforce and the patients they care for is important to meet language and other cultural needs of patients from different ethnic backgrounds. This article describes a study that examined internationally educated nurses’ transition experiences in the field of nursing with the objective of describing their unique contributions to the patient care experience. A review of the literature provides background information, followed by the study methods, findings, and discussion. Descriptive phenomenology guided this qualitative study that included 11 participants. Findings from this study illustrate how IENs perceive themselves as an asset to nursing and patient care. Implications for the future of nursing education, practice, research, and administration are offered. Healthcare providers that reflect the diversity of Canada’s population and can offer unique cultural perspective have potential to improve the patient experience during a hospital stay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".