Policy, Systems, and Environmental Change Strategies in the Supplemental Nutrition Assistance Program-Education (SNAP-Ed)
Bibliographic record
Abstract
OBJECTIVE: To categorize and quantify how states planned to use policy, systems, and environmental (PSE) change strategies in the Supplemental Nutrition Assistance Program-Education (SNAP-Ed). METHODS: Qualitative content analysis of SNAP-Ed annual plans from all 50 states, District of Columbia, Guam, and the US Virgin Islands between fiscal years 2014 and 2016. RESULTS: Between 2014 and 2016, the percentage of states that included PSEs as a statewide goal increased from 25% to 47%, and the percentage that planned to implement at least 1 PSE increased from 56% to 98%. Among states that planned to implement PSEs in 2016, the 3 most common settings were places in which people learn (92%), live (90%), and work (83%). CONCLUSIONS AND IMPLICATIONS: The increased planned use of PSEs in SNAP-Ed was considerable and encouraging as PSEs are important to use in conjunction with direct education and social marketing to improve nutrition and prevent obesity.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".