A multi-level, multi-component obesity intervention (Obesity Prevention and Evaluation of InterVention Effectiveness in NaTive North Americans) decreases soda intake in Native American adults
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of a multi-level, multi-component (MLMC) adult obesity intervention on beverage intake in Native American adults living in five geographically and culturally diverse tribal communities. DESIGN: A 14-month, community-randomised, MLMC design was utilised, with three communities randomised to Intervention and two communities randomised to Comparison. FFQ were administered pre- and post-interventions, and difference-in-differences (DiD) analysis was used to assess intervention impact on beverage intake. SETTING: The intervention took place within food stores, worksites, schools and selected media outlets located in the five communities. Key activities included working with store owners to stock healthy beverages, display and dispersal of educational materials, support of policies that discouraged unhealthy beverage consumption at worksites and schools and taste tests. PARTICIPANTS: Data were collected from 422 respondents between the ages of 18 and 75 living in the five communities pre-intervention; of those, 299 completed post-intervention surveys. Only respondents completing both pre- and post-intervention surveys were included in the current analysis. RESULTS: The DiD for daily servings of regular, sugar-sweetened soda from pre- to post-intervention was significant, indicating a significant decrease in Intervention communities (P < 0·05). No other changes to beverage intake were observed. CONCLUSIONS: Large, MLMC obesity interventions can successfully reduce the intake of regular, sugar-sweetened soda in Native American adults. This is important within modern food environments where sugar-sweetened beverages are a primary source of added sugars in Native American diets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".