A Qualitative Exploration of Approaches Applied by Nutrition Educators Within Nutrition Incentive Programs
Bibliographic record
Abstract
OBJECTIVE: To explore the approaches applied by nutrition educators who work with the US Department of Agriculture Gus Schumacher Nutrition Incentive Program (GusNIP), Nutrition Incentive (NI), and Produce Prescription (PPR) programs. METHODS: Multiple data collection methods, including descriptive survey (n = 41), individual interviews (n = 25), and 1 focus group (n = 5). Interviewees were educators who deliver nutrition education as a component of GusNIP NI/PPR programs. Descriptive statistics were calculated from survey responses. Transcripts were coded using thematic qualitative analysis methods. RESULTS: Four overarching themes emerged. First, educators have many roles and responsibilities beyond providing curriculum-based nutrition education. Second, interviewees emphasized participant-centered nutrition education and support. Third, partnerships with collaborating cross-sector organizations are essential. Fourth, there are common challenges to providing nutrition education within GusNIP NI/PPR programs, and educators proposed solutions to mitigate these challenges. CONCLUSIONS: Nutrition educators promote multilevel solutions to improve dietary intake, and it is recommended they be included in conversations to improve GusNIP NI/PPR programs.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".