Report on the impact of cultural diversity in simulation for nursing students engaged in immersion experiences in global settings
Bibliographic record
Abstract
Nursing students in the US are under increasing pressure to be equipped with appropriate knowledge and skills to meet an increasingly diverse American population. In order to meet this challenge, The American Association of Colleges of Nursing has charged nursing educators to incorporate cultural competence into nursing curriculums. Simulation scenarios focusing on assessment, including communication with clients from diverse cultures is a method to help students begin to practice safe nursing in a controlled environment. Nursing students in a baccalaureate program in the Midwest participated in scenarios designed to heighten their awareness of clues in the environment in order to interact with clients from different cultural backgrounds. The scenarios provided artifacts such as items of clothing, prayer rug, and statues that were consistent with specific cultures and helped direct the students to identify the need for an interpreter. Eighteen senior level students in the BSN program enrolled in an elective as part of an immersion experience in countries outside of the US. These students gained a better appreciation of the importance of obtaining culturally appropriate assessments in order to provide culturally competent care as evidenced by comments in their reflective journals.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".