The cultural safety of research reports on primary healthcare use by Indigenous Peoples: a systematic review
Bibliographic record
Abstract
Abstract Introduction Community-driven research in primary healthcare (PHC) may reduce the chronic disease burden in Indigenous peoples. This systematic review assessed the cultural safety of reports of research on PHC use by Indigenous peoples from four countries with similar colonial histories. Methods Medline, CINAHL and Embase were all systematically searched from 1st January 2002 to 4th April 2023. Papers were included if they were original studies, published in English and included data (quantitative, qualitative and/or mixed methods) on primary healthcare use for chronic disease (chronic kidney disease, cardiovascular disease and/or diabetes mellitus) by Indigenous Peoples from Western colonial countries. Study screening and data extraction were undertaken independently by two authors, at least one of whom was Indigenous. The baseline characteristics of the papers were analyzed using descriptive statistics. Aspects of cultural safety of the research papers were assessed using two quality appraisal tools: the CONSIDER tool and the CREATE tool (subset analysis). This systematic review was conducted in accordance with the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) tool. Results We identified 35 papers from Australia, New Zealand, Canada, and the United States. Most papers were quantitative (n = 21) and included data on 42,438 people. Cultural safety across the included papers varied significantly with gaps in adequate reporting of research partnerships, provision of clear collective consent from participants and Indigenous research governance throughout the research process, particularly in dissemination. The majority of the papers (94%, 33/35) stated that research aims emerged from communities or empirical evidence. We also found that 71.4% (25/35) of papers reported of using strengths-based approaches by considering the impacts of colonization on reduced primary healthcare access. Conclusion Research on Indigenous PHC use should adopt more culturally safe ways of providing care and producing research outputs which are relevant to community needs by privileging Indigenous voices throughout the research process including dissemination. Indigenous stakeholders should participate more formally and explicitly throughout the process to guide research practices, inclusive of Indigenous values and community needs.
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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 | 0.280 | 0.694 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 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; 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".