Community-Engaged Development of a Nutrition Curriculum: The Go Healthy Indigenous-Supported Agriculture Study
Bibliographic record
Abstract
OBJECTIVE: To understand how Osage Nation community members define healthy eating and develop a corresponding nutrition curriculum through community engagement. DESIGN: This project comprised a concurrent embedded mixed methods group concept mapping (GCM) study followed by focus group discussions (FGD) to provide feedback on a nutrition curriculum. SETTING: Osage Nation, Oklahoma. PARTICIPANTS: In the GCM study, 54 participants were recruited from a study of an Indigenous-supported agriculture program. GCM study participants and the Go Healthy Advisory Group participated in 2 FGDs. PHENOMENON OF INTEREST: For the first study, concepts related to healthy eating were explored. In the second study, participants provided feedback on curriculum clarity, perceived efficacy, and relevance. ANALYSIS: In the first study, a multidimensional scaling algorithm was used to plot statements, and concept clusters were interpreted in a facilitated meeting with participants. A basic content analysis approach was used in the second study. RESULTS: Five concept clusters related to healthy eating were identified. These clusters were used to generate an 8-module curriculum deemed clear, potentially efficacious, and relevant among FGD participants. CONCLUSIONS AND IMPLICATIONS: Community members contributed to a framework of healthy eating for the Osage community, which was used to develop a nutrition curriculum that will be integrated into an Indigenous-supported agriculture program. Future research should explore long-term sustainability and the broader cultural impacts of nutrition programs on Indigenous health and food sovereignty.
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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 | 0.004 | 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 | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".