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Record W3080654924 · doi:10.5430/jnep.v10n12p30

The evolution of cultural competence theories in American (United States) nursing curricula: An integrative review

2020· article· en· W3080654924 on OpenAlexvenueno aff
Suzanne Alexander, Rhonda BeLue, Ashley Kuzmik, Marie Boltz

Bibliographic record

VenueJournal of Nursing Education and Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsCurriculumCompetence (human resources)Psychological interventionPsychologyCultural competenceEssentialismNursingMedical educationPedagogyMedicineSociologySocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Baccalaureate nursing students develop cultural competence through curricula of theories and frameworks which evolve to reflect new knowledge, but their synthesis and impact upon health quality outcomes is not known. METHODS: A cross-platform literature review was conducted to identify innovation and use of cultural competency theories and frameworks in nursing. Optimal literature included a formal theory, pedagogy, measures, and outcomes, which were then classified and evaluated. Additional perspectives and interventions were reviewed for potential influence on curricula and impact through the lens of integrative review. RESULTS: A shift in theory from essentialism to constructivism has occurred in undergraduate curricula. Challenges to measuring outcomes have been noted. All studies reported positive outcomes but suffer from self-selection, unvalidated instruments, and little to no longitudinal data. CONCLUSIONS: Nursing students are exposed to culturally competent care via several validated and canonical frameworks, but self-efficacy and long-term impact have not been assessed.

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)0.009
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.009
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.487
Teacher spread0.411 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2020
Admission routes1
Has abstractyes

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