Closer to the Root: The Potential of Traditional Food Systems Interventions for Obesity Prevention
Bibliographic record
Abstract
We review the project design, implementation experiences, and study findings from obesity prevention trials (OPTs) in Native American communities to identify future directions for obesity and chronic disease prevention. We use a case study methodology to highlight three OPTs and identify themes across studies. We included three OPTs: Navajo Healthy Stores (NHS), OPREVENT1, and OPREVENT2. NHS was a food-store based environmental intervention, while OPREVENT1 and OPREVENT2 were multi-level, multi-component interventions with store, school, worksite, and media components (OPREVENT1) and combined with a policy-based component (OPREVENT2). All studies showed mixed success in achieving behavioral and health outcomes. NHS intervention exposure was associated with reduced BMI, and found improvements in intentions, cooking methods, and food getting comparing intervention with control regions. The program was sustained for several years after the trial was completed. OPREVENT1 trial resulted in significant reductions in waist circumference, reduced soda consumption, and increased physical activity (PA) comparing intervention and comparison communities. The OPREVENT2 trial had significant reductions in PA, calorie intake, carbohydrate, and dietary fat intake comparing intervention and comparison communities. Working with health staff can enhance intervention sustainability, which is also impacted by health center capacity and funding. All studies had strong evaluation designs, with high retention and low refusal rates. All studies included store components that aimed to improve healthy food access on-reservation and point-of-purchase promotion. Although all studies demonstrated impacts on behaviors and modest impacts on health outcomes, taking a food-systems approach could increase the likelihood of improved outcomes by addressing the root of healthy food access and obesity: diminished traditional food systems. These interventions hold great potential for supporting significant health outcomes by supporting traditional food-getting practices, preparation, and intake. Such interventions require strong community-academic partnerships since these food systems and knowledge belong to tribal nations and communities. NHLBI & USDA.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".