NEFPAT Plus: A Valid and Reliable Tool for Assessing the Nutrition Environment in Food Pantries
Bibliographic record
Abstract
OBJECTIVE: To develop a consumer nutrition environment assessment tool to assess policy, systems, and environmental initiatives that are implemented in food pantries, which incorporates recent national guidance, and evaluate its validity and reliability. SETTING: Illinois, US. DESIGN: This study had 4 phases: (1) tool revision, (2) pilot testing, (3) content validity assessment, and (4) interrater and test-retest reliability assessment. The original Nutrition Environment Food Pantry Assessment Tool (NEFPAT) was revised to incorporate evidence from updated guidelines and evidence. The NEFPAT+ was pilot-tested by 9 professionals at 5 food pantries. After revisions, 18 experts rated the content validity. Interrater and test-retest reliability was based on 2-4 professionals completing independent evaluations at 21 food pantries twice, 1 month apart. ANALYSIS: Content validity indices and intraclass correlation (ICC) coefficients for reliability estimates were compared with established thresholds. RESULTS: The NEFPAT+ was rated content valid by 94% of experts. The ICC for NEFPAT+ scores indicated excellent interrater reliability (ICC, 0.96; 99% confidence interval, 0.75-0.97) and good test-retest reliability (ICC: 0.80; 99% confidence interval, 0.60-0.92). CONCLUSIONS: Evidence supports the content validity, interrater reliability, and test-retest reliability of the NEFPAT+. Future studies can assess how NEFPAT+ scores relate to intervention outcomes and dietary behaviors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".