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Record W2331399770 · doi:10.2202/1548-923x.1919

Evaluating the Impact of a North American Nursing Exchange Program on Student Cultural Awareness

2011· article· en· W2331399770 on OpenAlexaffabout
Alice F. Kuehn, Andrea Chircop, Barbara Downe‐Wamboldt, Debbie Sheppard-LeMoine, Lucille Wittstock, Rosemary Herbert, Raquel Alicia Benavides-Torres, Donna Murnaghan, Kim Critchley

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of Prince Edward IslandDalhousie University
Fundersnot available
KeywordsCurriculumNursingNurse educationCultural competenceNursing researchHealth careTranscultural nursingCultural exchangeCultural diversityMedicinePsychologyMedical educationPolitical sciencePedagogySociology

Abstract

fetched live from OpenAlex

As the demand for cultural awareness in the provision of nursing care continues to increase, nursing programs must develop creative and effective teaching strategies and curricula to address this need. The evaluation of a five year, funded, North American nursing exchange project developed and implemented by six partner universities in Canada, Mexico and the United States of America is described in this article. The project was designed to enable nursing students to increase cultural awareness, redefine their role relationships with nurses from the partner countries, and increase knowledge regarding the health care systems and role of the nurse in those countries. Findings provide evidence that teaching nursing through a prism of cultural awareness, using both a jointly taught online course and student and faculty exchanges across the three countries is an effective strategy to increase the level of cultural awareness in nursing students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.362
GPT teacher head0.609
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2011
Admission routes2
Has abstractyes

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