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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".