Interventions to improve cultural competency in health care for Indigenous peoples of Australia, New Zealand, Canada and the USA: a systematic review
Bibliographic record
Abstract
PURPOSE: This article describes the characteristics and reviews the methodological quality of interventions designed to improve cultural competency in health care for Indigenous peoples of Australia, New Zealand, Canada and the USA. DATA SOURCES: A total of 17 electronic databases and 13 websites for the period of 2002-13. STUDY SELECTION: Studies were included if they evaluated an intervention strategy designed to improve cultural competency in health care for Indigenous peoples of Australia, New Zealand, the USA or Canada. DATA EXTRACTION: Information on the characteristics and methodological quality of included studies was extracted using standardized assessment tools. RESULTS OF DATA SYNTHESIS: Sixteen published evaluations of interventions to improve cultural competency in health care for Indigenous peoples were identified: 11 for Indigenous peoples of the USA and 5 for Indigenous Australians. The main types of intervention strategies were education and training of the health workforce, culturally specific health programs and recruitment of an Indigenous health workforce. Main positive outcomes reported were improvements in health professionals' confidence, and patients' satisfaction with and access to health care. The methodological quality of evaluations and the reporting of key methodological criteria were variable. Particular problems included weak study designs, low or no reporting of consent rates, confounding and non-validated measurement instruments. CONCLUSION: There is a lack of evidence from rigorous evaluations on the effectiveness of interventions for improving cultural competency in health care for Indigenous peoples. Future evaluations should employ more rigorous study designs and extend their measurement of outcomes beyond those relating to health professionals, to those relating to the health of Indigenous peoples.
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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 | 0.031 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".