Cultural adaptations and tailoring of public health nutrition interventions in Indigenous peoples and ethnic minority groups: opportunities for personalised and precision nutrition
Bibliographic record
Abstract
Indigenous peoples and ethnic minority groups often experience poor diet quality and poor health outcomes. Such inequities may be partially due to nutrition interventions not meeting the unique cultural and linguistic needs of these population groups, which could be achieved using co-creation and/or personalised approaches. Cultural adaptation or tailoring of nutrition interventions has shown promise in improving some aspects of dietary intake, but this requires careful consideration to ensure it does not inadvertently exacerbate dietary inequities. The aim of this narrative review was to examine examples of cultural adaptations and/or tailoring of public health nutrition interventions that improved the dietary intake and to consider implications for the optimal design and implementation of personalised and precision nutrition interventions. This review identified six examples of cultural adaptation and/or tailoring of public health nutrition intervention in Indigenous peoples and ethnic minority groups across Australia, Canada and the US. All studies used deep socio-cultural adaptations, such as the use of Indigenous storytelling, and many included surface-level adaptations, such as the use of culturally appropriate imagery in intervention materials. However, it was not possible to attribute any improvements in dietary intake to cultural adaptation and/or tailoring per se , and the minimal reporting on the nature of adaptations limited our ability to determine whether the interventions used true co-creation to design content or were adapted from existing interventions. Findings from this review outline opportunities for personalised nutrition interventions to use co-creation practices to design, deliver and implement interventions in collaboration with Indigenous and ethnic minority groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".