Cultural adaptation of health interventions including a nutrition component in Indigenous peoples: a systematic scoping review
Bibliographic record
Abstract
Abstract Background Indigenous populations throughout the world experience poorer health outcomes than non-indigenous people. The reasons for the health disparities are complex and due in part to historical treatment of Indigenous groups through colonisation. Evidence-based interventions aimed at improving health in this population need to be culturally safe. However, the extent to which cultural adaptation strategies are incorporated into the design and implementation of nutrition interventions designed for Indigenous peoples is unknown. The aim of this scoping review was to explore the cultural adaptation strategies used in the delivery of nutrition interventions for Indigenous populations worldwide. Methods Five health and medical databases were searched to January 2020. Interventions that included a nutrition component aimed at improving health outcomes among Indigenous populations that described strategies to enhance cultural relevance were included. The level of each cultural adaptation was categorised as evidential, visual, linguistic, constituent involving and/or socio-cultural with further classification related to cultural sensitivity (surface or deep). Results Of the 1745 unique records screened, 98 articles describing 66 unique interventions met the inclusion criteria, and were included in the synthesis. The majority of articles reported on interventions conducted in the USA, Canada and Australia, were conducted in the previous 10 years (n = 36) and focused on type 2 diabetes prevention (n = 19) or management (n = 7). Of the 66 interventions, the majority included more than one strategy to culturally tailor the intervention, combining surface and deep level adaptation approaches (n = 51), however, less than half involved Indigenous constituents at a deep level (n = 31). Visual adaptation strategies were the most commonly reported (n = 57). Conclusion This paper is the first to characterise cultural adaptation strategies used in health interventions with a nutrition component for Indigenous peoples. While the majority used multiple cultural adaptation strategies, few focused on involving Indigenous constituents at a deep level. Future research should evaluate the effectiveness of cultural adaptation strategies for specific health outcomes. This could be used to inform co-design planning and implementation, ensuring more culturally appropriate methods are employed.
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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 | 0.028 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".