How to measure cultural competence when evaluating patient-centred care: a scoping review
Bibliographic record
Abstract
Objectives The purpose of this study was to identify patient-centred quality indicators (PC-QI) and measures for measuring cultural competence in healthcare. Design Scoping review. Setting All care settings. Search strategy A search of CINAHL, EMBASE, MEDLINE, PsycINFO, Social Work Abstracts and SocINDEX, and the grey literature was conducted to identify relevant studies. Studies were included if they reported indicators or measures for cultural competence. We differentiated PC-QIs from measures: PC-QIs were identified as a unit of measurement of the performance of the healthcare system, which reflects what matters to patients and families, and to any individual that is in contact with healthcare services. In contrast, measures evaluate delivery of patient-centred care, in the form of a survey and/or checklist. Data collected included publication year and type, country, ethnocultural groups and mention of quality indicator and/or measures for cultural competence. Results The search yielded a total of 786 abstracts and sources, of which 16 were included in the review. Twelve out of 16 sources reported measures for cultural competence, for a total of 10 measures. Identified domains from the measures included: physical environment, staff awareness of attitudes and values, diversity training and communication. Two out of 16 sources reported PC-QIs for cultural competence (92 structure and process indicators, and 48 outcome indicators). There was greater representation of structure and process indicators and measures for cultural competence, compared with outcome indicators. Conclusion Monitoring and evaluating patient-centred care for ethnocultural communities allows for improvements to be made in the delivery of culturally competent healthcare. Future research should include development of PC-QIs for measuring cultural competence that also reflect cultural humility, and the involvement of ethnocultural communities in the development and implementation of these indicators.
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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 | 0.219 | 0.473 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.058 | 0.045 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".