OPREVENT2 Community Action Component to Promote Healthy Policy, Systems, and Environments With Native American Communities: Process Evaluation and Lessons Learned
Bibliographic record
Abstract
To assess the implementation of the Community Action Component (CAC) of OPREVENT2, which aimed to promote healthy food and physical activity policies, systems, and environments (PSE) with six Native American communities. OPREVENT2 examined the impact of a multi-level, multi-component obesity prevention intervention in six tribal communities in the Southwest and Midwest regions of the US (Three randomly selected to receive the intervention first, three to receive second). The CAC was designed to identify and develop PSE changes in partnership with community partners. We developed the following implementation standards prior to the intervention: we aimed to hold monthly meetings that were ≥1 hour and recruited ≥10 participants representing different stakeholder groups (health staff, store employee, tribal leader, school teachers/staff, and community members). We used measures of frequency to evaluate the extent that we met established reach, dose delivered, and fidelity standards. CAC meetings used participatory approaches to discuss ways to use PSE changes to encourage healthy eating and physical activity in stores, schools, worksites, and communities. Overall, we achieved high dose delivered; we held one monthly meeting on average that ranged 60–120 minutes. Reach varied by community; on average we attained 64%, 101%, 479% of the participants. Meetings were attended most often by community members, health staff, and tribal leaders, with low participation from store employees or school teachers/staff. Whenever possible, CAC meetings were planned with existing meetings to enhance reach to encourage sustainability and enable collaboration with community partners. Future interventions could develop a tribal council resolution to encourage other stakeholder groups to attend and promote sustainability of intersectoral work at an earlier stage. Assessing community readiness could assist in developing strategies to promote PSE changes that are tailored to each community. The inclusion of CAC complements community-based obesity prevention strategies in Native American communities and can achieve moderate to high reach, dose-delivered, and fidelity. National Heart, Lung, and Blood Institute.
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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 | 0.064 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".