Sugar-sweetened beverages and water intake among Indigenous youth in the United States and Canada: a scoping review of interventions
Bibliographic record
Abstract
The objectives of the scoping review were to: (1) conduct a systematic search for published literature focused on American Indian/Alaskan Native/Indigenous (AI/AN/I) youth, (2) identify current interventions that are focused on reducing sugar-sweetened beverages (SSB) or increased water intake (3) draw an understanding of who is leading these programmes, and (4) identify the lenses being used in developing and implementing the interventions. High consumption of sugar-sweetened beverages (SSBs) among youth is associated with numerous health problems, such as obesity, tooth decay, type 2 diabetes, and heart disease. Unless addressed early, many of these problems extend into, and/or present with additional complications in, adulthood. A comprehensive search was conducted across 5 electronic databases for peer-reviewed articles published until 20 March 2024. Additionally, manual searches were performed in 10 AI/AN/I-focused health journals. Data extraction was performed by 4 reviewers. Data management and analysis were performed using DistillerSR Inc. software, with screening and extraction conducted at all stages. Discrepancies were resolved by consensus among reviewers. The protocol for this scoping review was registered in the Open Science Framework (https://doi.org/10.17605/OSF.IO/MFZ8X). AI/AN/I youth, prenatal to 17 years of age, caregivers, and educators. The search yielded 13 studies that met the eligibility criteria. Interventions were predominantly implemented through school, community, and school-community approaches. Individuals with a range of skills and training levels delivered the interventions. Out of the 13 studies, only 2 studies in this scoping review comprise all 4 aspects of the cultural/spiritual, emotional, mental, and physical domains and characteristics of engaging Indigenous communities when conducting research. Interventions with AI/AN/I communities with strong community and Native Nation support have a greater chance of success regardless of community, home, or school settings. Importantly, AI/AN/I communities have distinct definitions of health, underscoring the importance of identifying these meanings and implementing them as appropriate within research designs.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.025 | 0.031 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| 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".