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Record W2774572552 · doi:10.3390/nu9121315

Eating at Food Outlets and “On the Go” Is Associated with Less Healthy Food Choices in Adults: Cross-Sectional Data from the UK National Diet and Nutrition Survey Rolling Programme (2008–2014)

2017· article· en· W2774572552 on OpenAlexafffund
Nida Ziauddeen, Eva Almiron‐Roig, Tarra L. Penney, Sonja Nicholson, Sara Kirk, Polly Page

Bibliographic record

VenueNutrients · 2017
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsDalhousie University
FundersMedical Research CouncilCanadian Institutes of Health ResearchUniversidad de NavarraPublic Health England
KeywordsEnvironmental healthObesityCross-sectional studyPsychological interventionFood groupConsumption (sociology)Public healthMedicineFood choiceGerontology

Abstract

fetched live from OpenAlex

Eating location has been linked with variations in diet quality including the consumption of low-nutrient energy-dense food, which is a recognised risk factor for obesity. Cross-sectional data from 4736 adults aged 19 years and over from Years 1–6 of the UK National Diet and Nutrition Survey (NDNS) Rolling Programme (RP) (2008–2014) were used to explore food consumption patterns by eating location. Eating location was categorized as home, work, leisure places, food outlets and “on the go”. Foods were classified into two groups: core (included in the principal food groups and considered important/acceptable within a healthy diet) and non-core (all other foods). Out of 97,748 eating occasions reported, the most common was home (67–90% of eating occasions). Leisure places, food outlets and “on the go” combined contributed more energy from non-core (30%) than from core food (18%). Analyses of modulating factors revealed that sex, income, frequency of eating out and frequency of drinking were significant factors affecting consumption patterns (p < 0.01). Our study provides evidence that eating patterns, behaviours and resulting diet quality vary by location. Public health interventions should focus on availability and access to healthy foods, promotion of healthy food choices and behaviours across multiple locations, environments and contexts for food consumption.

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)0.001
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 score0.008
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.104
GPT teacher head0.337
Teacher spread0.233 · 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

Citations37
Published2017
Admission routes2
Has abstractyes

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