Addressing a global nursing perspective in an undergraduate nursing program: Student learning in clinical education
Bibliographic record
Abstract
Objective: Although many educational student activities addressing global awareness are highlighted in the literature, the global nursing approach and how it is applied by students in clinical education is not widely described. After the implementation of a new global nursing curriculum, nursing students educated at The Swedish Red Cross University College are now engaged in counteracting inequalities in health. This paper aims to describe how nursing students apply the global nursing perspective during their clinical education.Methods: The study is based on students’ written individual reflective reports. The procedure for data analysis was inspired by a thematic and interpretive data synthesis. The four stages in Kolb’s learning cycle was used as a framework.Results: Four themes were identified: 1) Experiencing frailty, suffering and vulnerability; 2) Advocating quality of life and priorities in health; 3) Conceptualizing autonomy, involvement and participation; 4) Making a difference and acting with respect and an open mind.Conclusions: Consequently, students at the The Swedish Red Cross University College are confident in applying global nursing perspective in care actions. Nursing educators have a mutual responsibility to facilitate students’ knowledge transfers in global competencies and strategies to reduce the impact on the environment and on humans.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".