Exposure to specific components of a diabetes risk behavior prevention program associated with select psychosocial, dietary and anthropometric outcomes
Bibliographic record
Abstract
We implemented a multi‐institutional diabetes risk behavior prevention program in 7 First Nations in northwestern Ontario using culturally relevant community‐, school‐ and store‐based components. Data collected in intervention communities ( n =56 pre‐post respondents) were analyzed to assess the impact of exposure (adjusted for baseline value) on psychosocial outcomes, dietary behaviors and body mass index (BMI). Individual intervention components varied considerably with print media having higher levels of exposure. Exposure to shelf labels had a positive impact on dietary intention (β = 0.217; p=0.017). Dietary intention (β =0.204; p=0.045) and outcome expectations (β =0.224; p=0.019) were also predicted by exposure to the logo. Exposure to flyers was a significant predictor of dietary knowledge (β =0.023; p=0.022) while self‐efficacy was associated with exposure to posters (β =0.0300; p=0.051). Participation in community events was negatively associated with decreased BMI (β = −0.312; p=0.049) after controlling for known predictors. However, overall exposure to the program was not significantly associated with any outcomes, indicating that specific intervention channels are associated with specific outcomes. Intervention programs aimed at changing psychosocial factors and behaviors require greater exposure to more varied intervention components to have a positive impact.
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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 | 0.002 |
| 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 | 0.001 | 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".