Linking Global Citizenship, Undergraduate Nursing Education, and Professional Nursing
Bibliographic record
Abstract
In Brief As we move into the 21st century, our roles as nurses are becoming more complex. Inequities in health within and across nations demand that nursing students examine the interconnectedness between local and global health challenges and contribute to the development and implementation of solutions to these challenges. In this article, we examine concepts related to global citizenship, globalization, social responsibility, and professionalism and link them to curricular innovation in nursing education. We argue that the development of global citizenship is a fundamental goal for all nursing students and that to achieve this, nurse educators must move beyond the creation of international placement opportunities or the use of global examples within existing courses. Nurse educators must develop strategies and design innovative curricula to provide opportunities for all students to become engaged with the concept of global citizenship and the role of nurses in a global world. In this manuscript, the authors argue that the development of global citizenship is a fundamental goal for all nursing students. Nurse educators must develop strategies and design innovative curricula to provide opportunities for all students to become engaged with the concept of global citizenship and the role of nurses in a global world. www.advancesinnursingscience.com
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".