Internationally educated nurses’ reflections on nursing communication in Canada
Bibliographic record
Abstract
AIM: The overall goal of this study was to explore internationally educated nurses' perceptions of the English language and nursing communication skill requirements in a Canadian bridging education program. BACKGROUND: The increased global mobility of nurses creates a need to address the educational needs of migrating nurses. A large percentage of these nurses require additional language and professional education. New research is needed that would represent an in-depth analysis of their educational experiences associated with learning academic English and Canadian nursing communication. INTRODUCTION: Developing proficiency with a new language has been documented as posing challenges for new immigrants. Since language proficiency is a key requirement of Canadian nursing regulatory bodies, previously unrecognized barriers such as attitudes and beliefs about required English language and nursing communication competency which may hinder their ability to meet local practice standards need to be explored. METHOD: Using a grounded theory study design, narratives from 22 participants from the Philippines, Nigeria and Europe enrolled in bridging education were collected and analysed. RESULTS: The participants identified the incongruence in professional norms between Canada and their home country as a major challenge. The major themes identified included cultural dissonance, academic literacy challenges and skepticism regarding unexpected communication competency requirements. DISCUSSION: The participants possessed varying degrees of comprehension and acceptance of new educational and professional regulatory requirements. A certain degree of culture shock, which may be associated with frustration and disillusionment, is a typical and anticipated aspect of the immigration process. Their perceptions need to be recognized and accommodated when assisting internationally educated nurses to integrate into the Canadian practice culture. LIMITATIONS OF THE STUDY: Any generalizations to other host countries need to be made cautiously. CONCLUSION AND IMPLICATIONS FOR NURSING POLICY: Clear communication from regulators about English language and nursing communication requirements during the pre-arrival period is recommended. If bridging education is required, these programs need to be designed to address English language competency and nursing communication skills of non-native English speakers.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".