Improving public health data collection approaches across populations: findings from a national evaluation of fruit and vegetable incentives
Bibliographic record
Abstract
OBJECTIVE: Public health approaches for addressing diet-related health in the USA include nutrition incentive (NI) and produce prescription (PPR) projects. These projects, funded through the US Department of Agriculture Gus Schumacher Nutrition Incentive Program (GusNIP), aim to support the intake of fruits and vegetables through healthy food incentives. Measuring the GusNIP impact is vital to assessing the ability of incentives to improve public health nutrition outcomes across populations. Shared measures used across GusNIP projects assess fruit and vegetable intake, food security and demographics, among other variables, through a participant survey. This study explored challenges and opportunities to evaluation across populations within a national public health oriented program, GusNIP. DESIGN: This qualitative study used a sociodemographic survey, semi-structured interviews and focus groups. Descriptive statistics were used to summarise survey data, and applied thematic analysis was used to identify patterns in interview and focus group data. SETTING: Data collection occurred in the USA virtually using Qualtrics and Zoom from fall 2021 to fall 2022. PARTICIPANTS: Eighteen GusNIP PPR and NI data collectors, twenty-four external evaluators and eleven GusNIP National Training, Technical Assistance, Evaluation, and Information Center staff participated. RESULTS: Opportunities to improve evaluation among GusNIP's participants include tailoring surveys to specific subpopulations, translations, culturally appropriate food examples, avoiding stigmatising language, using mixed methods and intentional strategies to enhance representation. CONCLUSION: To increase applicability of data collection in public health programs, evaluation tools must reflect the experiences across populations. This study provides insights that can guide future NI, PPR and public health evaluations, helping to more effectively measure and understand outcomes of all communities.
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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 | 0.357 | 0.385 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".