Constructs of Health and Environment Inform Child Obesity Prevention in American Indian Communities
Bibliographic record
Abstract
OBJECTIVE: Obesity prevention efforts have had limited success in American Indian (AI) populations. More effective prevention programs might be designed using insights into linkages between parental health beliefs, environmental constraints and healthy lifestyle choices. METHODS AND PROCEDURES: Focus group sessions (n = 42 participants) were conducted to explore parental perspectives on children's health, diet and physical activity in three Wisconsin Tribal communities. Focus group questions were derived from preliminary interviews and observations on environmental barriers surrounding nutrition and physical activity. RESULTS: Two broad thematic areas emerged from the focus groups: child health themes and environmental themes. Health themes included views of child health (emphasizing emotional health), views on parenting, and assessment of risks to child safety. Environmental (social and physical) themes included assessments of personal support networks, assessments of local facilities and programs, and values regarding household relationships. A provisional model of family behaviors related to child nutrition and physical activity was developed to better understand these themes and the potential tensions among them. DISCUSSION: Understanding the unique cultural constructs of health and environment of AI communities can inform decision making in community-level prevention research. The proposed model served as a useful starting point for designing healthy lifestyle interventions in these AI communities. This model may also be applicable to other minority communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".