Journey to Promoting Structural Change for Chronic Disease Prevention: Examining the Processes for Developing Policy, Systems, and Environmental Supports in Native American Nations
Bibliographic record
Abstract
Background: Obesity and chronic disease rates continue to be disproportionally high among Native Americans (NAs) compared with the US general population. Policy, systems, and environmental (PSE) changes can address the root causes of these health inequalities by supporting access to healthy food and physical activity resources. Objective: We aim to describe the actors and processes involved in developing PSE changes supporting obesity prevention in NA Nations. Methods: As part of the Obesity Prevention Research and Evaluation of InterVention Effectiveness in NaTive North Americans 2 (OPREVENT2) trial (ClinicalTrials.gov registration: NCT02803853), we collected 46 in-depth interviews, 1 modified Talking Circle, 2 workshops, and 14 observations in 3 NA communities in the Midwest and Southwest regions of the United States. Participants included Tribal government representatives/staff, health staff/board members, store managers/staff, and school administrators/staff. We used a Grounded Theory analysis protocol to develop themes and conceptual framework based on our data. Results: Health staff members were influential in identifying and developing PSE changes when there was a strong relationship between the Tribal Council and health department leaders. We found that Tribal Council members looked to health staff for their expertise and were involved in the approval and endorsement of PSE changes. Tribal grant writers worked across departments to leverage existing initiatives, funding, and approvals to achieve PSE changes. Participants emphasized that community engagement was a necessary input for developing PSE changes, suggesting an important role for grassroots collaboration with community members and staff. Relevant contextual factors impacting the PSE change development included historical trauma, perspectives of policy, and "tribal politics". Conclusions: This article is the first to produce a conceptual framework using 3 different NA communities, which is an important gap to be addressed if structural changes are to be explored and enacted to promote NA health. The journey to change for these NA Nations provides insights for promoting future PSE change among NA Nations and 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 | 0.050 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".