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Record W2607263544 · doi:10.5430/jnep.v7n9p90

Cultural competence of pre-licensure nursing faculty

2017· article· en· W2607263544 on OpenAlexvenueno aff
Colleen Marzilli, Beth Mastel‐Smith

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsLicensureCompetence (human resources)PsychologyQualitative propertyDescriptive statisticsCultural competenceMedical educationExperiential learningCertificationQualitative researchNursingMedicinePedagogySocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the level of cultural competence (CC) in Texas pre-licensure nursing faculty and examine the relationships between demographics and CC scores. The researchers conducted a study to determine if demographics predicted the level of CC and explored the perceptions of CC. A convergent parallel mixed-methods design used data from a 2014 online survey with a qualitative interview component. Demographics were evaluated with descriptive statistics and CC was measured with The Nurses’ Cultural Competence Scale (NCCS). Qualitative data were analyzed using a constant comparative method. The level of CC was low to moderate. Three themes emerged from the interviews: knowledge is experiential, skills require emotional intelligence, and desire requires a catalyst. Nursing faculty could benefit from experiences with culturally diverse patients and students. Continuing education offerings and courses should follow best practices models of CC education and focus on providing meaningful experiences may also increase the knowledge and skills to help faculty members.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.219
GPT teacher head0.552
Teacher spread0.333 · 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 teacher head, not a consensus.

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

Citations7
Published2017
Admission routes1
Has abstractyes

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