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Record W2039213283 · doi:10.12927/hcq.2011.22578

Enhancing Cultural Competence for Improved Access to Quality Care

2011· article· en· W2039213283 on OpenAlexaff
Karima Karmali, Linda Grobovsky, Jennifer Levy, Margaret Keatings

Bibliographic record

VenueHealthcare Quarterly · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsSickKids Foundation
Fundersnot available
KeywordsCultural competenceExcellenceNursingHealth careMedicineCompetence (human resources)Health equityBest practiceQuality managementCurriculumMedical educationPsychologyPublic healthService (business)BusinessPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

The Hospital for Sick Children (SickKids) is situated in one of the most diverse cities in the world. This is reflected in the patient population it serves. In 2009, the hospital embarked on a quality improvement initiative to address the existing evidence base on health disparities and to enhance health equity through cultural competence programming. The goal was to achieve optimal health outcomes for all patients and families, with a particular focus on new immigrant and other vulnerable populations. Evaluation results indicate changes in clinical practice as a result of this initiative and increased patient satisfaction with regard to staff members' level of cultural sensitivity. This article provides an overview of this hospital-wide initiative, as well as the evaluation methods and outcomes. Based on a needs assessment, we developed an institutionally meaningful curriculum with SickKids' values of family-centred care, patient safety and service excellence embedded in the program. Educational sessions were delivered to clinical and non-clinical hospital staff, focusing on health disparities, the case for culturally competent care and practical tools for healthcare practitioners. Organizational change strategies, including the use of champions as change agents and role models, were used to embed cultural competence as integral to family-centred care at SickKids.

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)0.000
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 score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.147
GPT teacher head0.447
Teacher spread0.300 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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