Monday Poster Session
Bibliographic record
Abstract
INTRODUCTION: Low consumption of fruit and vegetables is a risk factor for poor health.Some studies have shown consumption varies across neighbourhoods, with lower intake in disadvantaged neighbourhoods.However, findings are far from consistent.Such inconsistencies suggest that socio-spatial inequities in diet may be context-specific, highlighting a need for international comparisons across contexts.Our study examined variations in fruit and vegetable consumption among adults living in neighbourhoods of varying socioeconomic status (SES) across seven countries (Australia, New Zealand, Canada, Netherlands, USA, Scotland, Portugal).METHODS: This study used data from seven existing studies with key variables assessed in adults from neighbourhoods of varying SES.Data were harmonised and logistic regression was used to examine associations between neighbourhood SES and binary fruit and vegetable consumption separately, adjusting for neighbourhood clustering and age, gender and education.RESULTS: Analyses showed evidence of an association between neighbourhood SES and fruit consumption (P < 0.05) in New Zealand, Canada and Scotland.Results showed increased odds of fruit intake in higher SES areas.Results for vegetable intake were less consistent.In Australia, New Zealand and Canada, there was evidence of reduced odds of vegetable consumption for those residing in low SES areas, while in Portugal adults in the highest SES areas had lowest odds of consumption.The other studies showed no difference by SES.CONCLUSIONS: This study highlights that associations between diet and neighbourhood deprivation vary across countries.Neighbourhood environments have the potential to influence healthy behaviour and further research is required to examine the context in which these associations arise.It may be that differential access to resources in which this produce is available was a factor explaining the associations in this study.However, it is important to acknowledge discrepancies across the studies in terms of sampling, measures, and definitions of neighbourhoods, meaning we cannot draw strong conclusions.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.884 | 0.756 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".