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Record W4407737932 · doi:10.1186/s12889-025-21855-9

An environmental scan of financial incentives to increase access to healthy foods: How, how much, and how often?

2025· article· en· W4407737932 on OpenAlexafffundabout
Sendeku Yohannes, Dana Lee Olstad, David J.T. Campbell, Crystal Corrigan, Reed F. Beall

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsBiostatisticsMedicineIncentivePublic healthEnvironmental healthEpidemiologyFinanceInternal medicineBusinessEconomicsPathologyMicroeconomics

Abstract

fetched live from OpenAlex

This environmental scan identified four main incentive structures for promoting healthy food purchases: price discounts, food vouchers, rewards, and hybrid structures. The median weekly incentive value across all programs was $14.38 USD, with significant variation. Incentives were most commonly distributed on a weekly basis (29%). Few programs provided evidence-based justifications for their chosen incentive structures, values, or distribution frequencies. Further research is needed to directly compare the effectiveness of different incentive structures, values, and frequencies in promoting healthier food purchases. Diet quality significantly influences chronic disease prevalence. Interventions that promote healthier food purchases through economic incentives are gaining attention, but there is limited information on the practical aspects of implementing these programs as a health promotion strategy in retail grocery store settings. This study explores common incentive structures, their monetary values, and distribution frequencies. A structured environmental scan of academic and grey publications from January 2010 to August 2021 was conducted. Sources from Canada, the United States, the United Kingdom, New Zealand, and Australia were included if they described interventions initiated by organizations to reduce the cost of healthier foods in retail grocery stores without requiring a prescription or disease diagnosis. Data were extracted in duplicate and synthesized narratively, focusing on the type, value, and how often incentives were distributed. The median, rather than the mean, was calculated to account for skewed distributions of incentive values across programs. Monetary values were standardized to weekly amounts and adjusted to 2024 US dollars. From 4,953 academic and 40 grey literature sources, 17 programs were identified. These programs featured four incentive structures: price discounts (41%), food vouchers (24%), rewards (18%), and hybrid structures (18%). The median incentive value was $14.38 per week. Incentives were typically provided weekly (29%) or monthly (18%), with some offered per-shop, per-day, or once per program. Considerable heterogeneity was observed in incentive structures, values, and frequencies. Justifications for these designs were often lacking, highlighting the need for further research that directly compares the impact of different incentive structures, amounts, and frequencies on food purchases. Until more empirical evidence is available, program design choices can be guided by precedent, modeled after policy options under consideration (e.g., exempting beneficiaries from general sales tax on fresh produce), or built upon successful existing models, such as the United States Department of Agriculture’s Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Program.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.125
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0220.036
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.032
GPT teacher head0.327
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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