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Record W2019663794 · doi:10.3390/ijerph10083325

A Food Retail-Based Intervention on Food Security and Consumption

2013· article· en· W2019663794 on OpenAlexaff
Richard C. Sadler, Jason Gilliland, Godwin Arku

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsWestern University
Fundersnot available
KeywordsMalnutritionEnvironmental healthIntervention (counseling)Psychological interventionFood securityConsumption (sociology)Public healthObesityPopulationBusinessCausality (physics)MedicineGeographyEconomicsEconomic growthAgriculture

Abstract

fetched live from OpenAlex

The effect of the built environment on diet (and ensuing health outcomes) is less understood than the effect of diet on obesity. Natural experiments are increasingly advocated in place of cross-sectional studies unable to suggest causality. The central research question of this paper, therefore, asks whether a neighborhood-level food retail intervention will affect dietary habits or food security. The intervention did not have a significant impact on fruit and vegetable consumption, and the intervention population actually purchased prepared meals more frequently. More problematic, only 8% of respondents overall regularly consumed enough fruits and vegetables, and 34% were food insecure. Further complicating this public health issue, the new grocery store closed after 17 months of operation. Results indicate that geographic access to food is only one element of malnutrition, and that multi-pronged dietary interventions may be more effective. The economic failure of the store also suggests the importance of non-retail interventions to combat malnutrition.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.080
GPT teacher head0.316
Teacher spread0.236 · 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 designNon-randomized trial
Domainnot available
GenreEmpirical

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

Citations97
Published2013
Admission routes1
Has abstractyes

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