Food Perceptions and Dietary Behavior of American-Indian Children, Their Caregivers, and Educators: Formative Assessment Findings from Pathways
Bibliographic record
Abstract
Dietary findings from a school-based obesity prevention project (Pathways) are reported for children from six different American-Indian nations. A formative assessment was undertaken with teachers, caregivers, and children from nine schools to design a culturally appropriate intervention, including classroom curriculum, food service, physical education, and family components. This assessment employed a combination of qualitative and quantitative methods (including direct observations, paired-child in-depth interviews, focus groups with child caregivers and teachers, and semistructured interviews with caregivers and foodservice personnel) to query local perceptions and beliefs about foods commonly eaten and risk behaviors associated with childhood obesity at home, at school, and in the community. An abundance of high-fat, high-sugar foods was detected in children's diets described by caregivers, school food-service workers, and the children themselves. Although children and caregivers identified fruits and vegetables as healthy food choices, this knowledge does not appear to influence actual food choices. Frequent high-fat/high-sugar food sales in the schools, high-fat entrees in school meals, the use of food rewards in the classroom, rules about finishing all of one's food, and limited family resources are some of the competing factors that need to be addressed in the Pathways intervention.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".