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Enhancing cultural competence: Trans‐Atlantic experiences of European and Canadian nursing students

2009· article· en· W2073833657 on OpenAlexaffabout
Liisa Koskinen, Barbara Campbell, Clara Aarts, France Chassé, Ann Hemingway, Tiina Juhansoo, Maureen Mitchell, France L Marquis, Kim Critchley, Pamela M. Nordstrom

Bibliographic record

VenueInternational Journal of Nursing Practice · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsMount Royal UniversityUniversité de MonctonRoyal College of Physicians and Surgeons of CanadaUniversity of Prince Edward Island
FundersEuropean Commission
KeywordsCultural competenceCultural diversityRealmNursingCompetence (human resources)Health carePsychologyPedagogySociologyMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

This paper describes the enhancement of cultural competence through trans-Atlantic rural community experiences of European and Canadian nursing students using critical incident technique (CIT) as the students' reflective writing method. The data generated from 48 students' recordings about 134 critical incidents over a 2-year project were analysed by qualitative content analysis. Five main learning categories were identified as: cross-cultural ethical issues; cultural and social differences; health-care inequalities; population health concerns; and personal and professional awareness. Four emergent cultural perspectives for the health sector that became apparent from the reflections were: health promotion realm; sensitivity to social and cultural aspects of people's lives; channels between the health sector and society; cultural language and stories of local people. CIT was successfully used to foster European and Canadian undergraduate students' cultural reflections resulting in considerations and suggestions for future endeavours to enhance cultural competence in nursing education.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0350.013
Scholarly communication0.0090.002
Open science0.0020.012
Research integrity0.0030.005
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 head0.048
GPT teacher head0.440
Teacher spread0.393 · 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 designQualitative
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

Citations33
Published2009
Admission routes2
Has abstractyes

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