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Record W4403620672 · doi:10.21833/ijaas.2024.09.019

Nurses' cultural competence and its impact on work engagement and teamwork in community medical centers healthcare network in Central California, USA

2024· article· en· W4403620672 on OpenAlexaff
Gemma Stela L. Imperial, Evelyn E. Feliciano, Alfredo Z. Feliciano, Mary Angelica P. Bagaoisan, Cyrelle D. Agunod, Delma Joie D. Magtubo, Anna Lyn M. Paano

Bibliographic record

VenueInternational Journal of ADVANCED AND APPLIED SCIENCES · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsNorthern College
Fundersnot available
KeywordsTeamworkCompetence (human resources)Health carePsychologyWork (physics)NursingCultural competenceMedical educationPolitical scienceMedicineEngineeringPedagogySocial psychology

Abstract

fetched live from OpenAlex

This study explores the relationship between nurses' cultural competence and their work engagement and teamwork in hospitals at a tertiary private medical center in Central California. Using a correlational method, data were collected from 357 nurses, primarily female, aged 37-38, and of Asian descent. The Self-Assessment Cultural Competence Checklist, Utrecht Work Engagement Scale (WES-9), and Nursing Teamwork Scale (NTS) were used to measure these variables. Pearson’s correlation analysis revealed a positive association between cultural competence, work engagement, and teamwork, indicating that differences in beliefs can enhance collaboration and team cohesion among healthcare workers from diverse backgrounds. These findings highlight the importance of cultural competence in fostering effective teamwork and relationships in a multicultural healthcare environment.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.407
Teacher spread0.368 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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