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Record W4394587643 · doi:10.53555/jptcp.v29i04.5449

IMPACT OF CULTURAL COMPETENCE ON HEALTHCARE OUTCOMES IN SAUDI ARABIA

2022· article· en· W4394587643 on OpenAlexaff
Essa Abdullah A Aljawyad, Maryam Ahmed Alkadi, Nasser Hussian Almosharaf, Abdullah saud B aljohani, Waleed Mohammed Ali AL Abbood, Yousef Saleh Saleh Alhasawi, Ali Abdullahal Alyateem, Abdulmajeed ali ali alessa, Merai Ali Alsayed, Ashwaq Marzouq Alomayri, Ali Nassr Alzaher, Awadh Mohammed Al Antar, Zahra Abdullah A Aloyayd, Raed Abdullah Ali Alshahrani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsHealth careCultural competenceCompetence (human resources)PsychologyNursingMedicinePolitical sciencePedagogySocial psychology

Abstract

fetched live from OpenAlex

Cultural competence in healthcare has gained significant attention in recent years due to its potential to improve patient outcomes and enhance the quality of care. This review article explores the impact of cultural competence on healthcare outcomes in Saudi Arabia, a country known for its diverse population and unique cultural practices. By examining the existing literature on this topic, the article aims to provide insights into the importance of cultural competence in the Saudi healthcare system and its implications for patient care. The review will discuss the challenges and opportunities associated with integrating cultural competence into healthcare practices in Saudi Arabia, as well as the potential benefits for both patients and healthcare providers. Furthermore, the article will highlight the role of education and training in promoting cultural competence among healthcare professionals in Saudi Arabia and suggest recommendations for future research and policy development in this area.

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)0.002
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.072
GPT teacher head0.418
Teacher spread0.346 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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