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Record W2018645442 · doi:10.1017/s1368980009005369

Disparities in fruit and vegetable supply: a potential health concern in the greater Québec City area

2009· article· en· W2018645442 on OpenAlexaffabout
Nathalie Pouliot, Anne-Marie Hamelin

Bibliographic record

VenuePublic Health Nutrition · 2009
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDiversity (politics)GeographyDistribution (mathematics)SocioeconomicsEnvironmental healthFood supplyRural areaBusinessAgricultural scienceMedicineSociologyEnvironmental scienceMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study explores the spatial distribution and in-store availability of fresh fruits and vegetables from a socio-environmental perspective in terms of the type of food store, level of deprivation and the setting (urban/rural) where the food outlets are located. DESIGN: Seven types of fresh fruit and vegetable stores (FVS) were identified then visited in six districts (urban setting) and seven communities (rural setting). The quantity and diversity of fresh fruits and vegetables (F&V) were also assessed. SETTING: Québec City, Canada. RESULTS: The FVS spatial distribution showed differences between the two settings, with accessibility to supermarkets being more limited in rural settings. The quantity and diversity of fresh F&V in-store availability were associated with the type of FVS, but not with setting or its level of deprivation. Greengrocers and supermarkets offered a greater quantity and diversity of fresh F&V than the other FVS. CONCLUSIONS: The results suggest that inequalities in physical access to fresh F&V across the region could have an impact on public health planning considering that supermarkets, which are one of the excellent sources of F&V, are less prevalent in rural settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.051
GPT teacher head0.306
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2009
Admission routes2
Has abstractyes

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