Organisational systems’ approaches to improving cultural competence in healthcare: a systematic scoping review of the literature
Bibliographic record
Abstract
INTRODUCTION: Healthcare organisations serve clients from diverse Indigenous and other ethnic and racial groups on a daily basis, and require appropriate client-centred systems and services for provision of optimal healthcare. Despite advocacy for systems-level approaches to cultural competence, the primary focus in the literature remains on competency strategies aimed at health promotion initiatives, workforce development and student education. This paper aims to bridge the gap in available evidence about systems approaches to cultural competence by systematically mapping key concepts, types of evidence, and gaps in research. METHODS: A literature search was completed as part of a larger systematic search of evaluations and measures of cultural competence interventions in health care in Canada, the United States, Australia and New Zealand. Seventeen peer-reviewed databases, 13 websites and clearinghouses, and 11 literature reviews were searched from 2002 to 2015. Overall, 109 studies were found, with 15 evaluating systems-level interventions or describing measurements. Thematic analysis was used to identify key implementation principles, intervention strategies and outcomes reported. RESULTS: Twelve intervention and three measurement studies met our inclusion criteria. Key principles for implementing systems approaches were: user engagement, organisational readiness, and delivery across multiple sites. Two key types of intervention strategies to embed cultural competence within health systems were: audit and quality improvement approaches and service-level policies or strategies. Outcomes were found for organisational systems, the client/practitioner encounter, health, and at national policy level. DISCUSSION AND IMPLICATIONS: We could not determine the overall effectiveness of systems-level interventions to reform health systems because interventions were context-specific, there were too few comparative studies and studies did not use the same outcome measures. However, examined together, the intervention and measurement principles, strategies and outcomes provide a preliminary framework for implementation and evaluation of systems-level interventions to improve cultural competence. Identified gaps in the literature included a need for cost and effectiveness studies of systems approaches and explication of the effects of cultural competence on client experience. Further research is needed to explore the extent to which cultural competence improves health outcomes and reduces ethnic and racially-based healthcare disparities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".