Quantitative Measures Used to Evaluate Nutrition Incentive Programs in the United States: A Scoping Review
Bibliographic record
Abstract
This scoping review aimed toidentifyquantitative measures used to evaluate nutrition incentives (NIs), highlight potential evaluation gaps, and consider implications for future NI research and evaluation efforts. NIs for fruits and vegetables (FV) aim to increase fruit and vegetable intake (FVI) and food security among households with low income. Research and evaluation efforts among NIs may vary, which limits opportunities to make meaningful comparisons across programs and understand wide-scale impact. The Joanna Briggs Institute (JBI) guidelines and thePreferred Reporting Items for Systematic Reviews and Met-Analysis extension for Scoping Reviews (PRISMA-ScR) were used. Peer-reviewed and grey literature on quantitative measures used to evaluate NIs in the United States wasidentifiedamong four bibliographic databases and webpages. Data were extracted for NI measures across levels of the NI Theory of Change (TOC), including participant-level (dietary behaviors, food security status, self-efficacy, and food purchasing behaviors), site-level (eg, brick and mortar stores, farmers markets), partner-level (eg, retailers, farmers market vendors, farmers), process-level (eg, program compliance), and community-level (eg, neighborhood food environment). 127sources were included in the scoping review. Participant-level measures (n = 105) were the mostfrequentlyreported, specifically, sociodemographic characteristics (n = 86) and foodpurchasingbehaviors (n = 72). Site-level measures (n = 71) included topics ranging from participant awareness of the program to incentive redemption and distribution, with program awareness and/orutilizationthe most used (n = 34). Process (n = 9), partner (n = 16), andcommunity measures (n = 19) were the least reported. Most sources used non-psychometrically tested measurement tools. There is a need for measures that balance rigor, feasibility in practice, and alignment with NI program goals. Our findings suggest there is potential to standardizefrequentlyused quantitative measures across NI programs. Concerted efforts toidentifyshared measures may lead to increased understanding of NIs' ability to improve food security andequitableaccess to FVs.
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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 | 0.136 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.040 | 0.042 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".