Technical Assistance is Related to Improvements in the Food Pantry Consumer Nutrition Environment
Bibliographic record
Abstract
OBJECTIVE: To assess changes in food pantries' consumer nutrition environment (CNE) after the provision of technical assistance. DESIGN: Pre-post study with 2 phases. SETTING: Staff completed observational assessments using the Nutrition Environment Food Pantry Assessment Tool (NEFPAT) at food pantries in an initial pilot phase. Then, staff conducted NEFPAT observations at pantries in Illinois statewide. PARTICIPANTS: In the pilot phase, 6 staff assessed 28 pantries. In the statewide phase, 35 staff assessed 119 pantries. INTERVENTION: After completing an initial NEFPAT at each pantry, technical assistance was provided by staff to support changes in the pantries' CNE before another NEFPAT observation was completed. MAIN OUTCOME MEASURE: Changes in the CNE, as assessed with the NEFPAT, when comparing preassessment and postassessment. ANALYSIS: Score differences were evaluated with paired t tests. RESULTS: In the pilot phase, among 23 pantries with preassessment and postassessment data, 2 objectives on the NEFPAT observation increased significantly. In the statewide phase, among 66 pantries with preassessment and postassessment data, most NEFPAT objectives and the overall NEFPAT score (22.12 ± 8.16 vs 28.20 ± 7.14, P < 0.001) significantly increased. CONCLUSIONS AND IMPLICATIONS: Technical assistance provided by Supplemental Nutrition Assistance Program Education implementing staff were related to improvements in the CNE of food pantries in Illinois. Future work should evaluate the association of these CNE changes with changes in behavior among pantry patrons.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".