The SNAP-Ed Evaluation Framework: Nationwide Uptake and Implications for Nutrition Education Practice, Policy, and Research
Bibliographic record
Abstract
OBJECTIVE: Provide the first baseline census of Supplemental Nutrition Assistance Program Education (SNAP-Ed) state implementing agencies' (SIAs) intent to use and evaluate 51 indicators described in the SNAP-Ed Evaluation Framework. METHODS: A cross-sectional study design was used to administer electronic surveys to 124 SIAs who received SNAP-Ed funding in 2017. Descriptive statistics were used to analyze the results. RESULTS: Of 51 indicators, SIAs reported their intent to impact an average of 19 indicators and evaluate an average of 12. More SIAs reported the intention to impact indicators at the individual (59%) and environmental levels (48%), compared with the sectors of influence (20%) and population levels (30%) of the framework. In addition, more SIAs intended to impact and evaluate short- or medium-term indicators, compared with long-term indicators. CONCLUSIONS AND IMPLICATIONS: These findings illustrate the progress made toward aggregating metrics to measure the collective impact of SNAP-Ed.
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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 | 0.403 | 0.445 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".