Challenges in Oral Communication for Internationally Educated Nurses
Bibliographic record
Abstract
Achieving English language proficiency, while key to successful adaptation to a new country for internationally educated nurses (IENs), has presented more difficulties for them and for educators than previously recognized. Professional communication within a culturally diverse client population and maintaining collaborative relationships between nurses and other team members were perceived as new challenges for IENs. Learning an additional language is a long-term, multistage process that must also incorporate social and cultural aspects of the local society and the profession. This article provides a descriptive review of current research literature pertaining to English language challenges, with a focus on oral language, experienced by IENs. Educational strategies for teaching technical language skills as well as the socio-pragmatics of professional communication within nursing programs are emphasized. Bridging education programs must not only develop students'academic language proficiency but also their ability to enter the workforce with the kind of communication skills that are increasingly highlighted by employers as essential attributes. The results of this review are intended to facilitate a clearer understanding of the English language and communication challenges experienced by IENs and identify the implications for designing effective educational programs.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".