Professional Nurses as Global Citizens: Developing an Integrated Approach in Undergraduate Nursing Curricula
Bibliographic record
Abstract
As university and nursing programs place more emphasis on global perspectives in their strategic plans and goals, it is frequently unclear how these perspectives are integrated into curricula.With globalization processes impacting health and education throughout the world, it is timely to understand the current context in order to move ahead to promote the development of professional nurses as global citizens.The interpretive descriptive study explored the characteristics and qualities of global citizenship and global health from the perspective of 12 expert informant nursing education leaders from undergraduate nursing programs within Canada.The study also examined curricular and pedagogical directions that would promote the integration of global citizenship education within nursing programs.In addition to the interview as the main data collection tool, field notes and a reflective journal was used in data gathering.The findings are presented as qualitative description and were analyzed using the interpretive description method.The findings of this study revealed several characteristics of global citizenship and curricular directions for global citizenship education, including particular courses, curricular lenses and a variety of concepts.Pedagogical strategies were described as well as the challenges to the integration of these approaches for nursing education.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".