Diet Quality and Neighborhood Environment in the Atlantic Partnership for Tomorrow’s Health Project
Bibliographic record
Abstract
An understanding of relationships between different constructs of the neighbourhood environment and diet quality is needed to inform public health interventions. This study investigated associations between material deprivation, social deprivation and population density with diet quality in a cohort of 19,973 Canadian adults aged 35 to 69 years within the Atlantic PATH cohort study. Diet quality, a metric of how well diet conforms to recommendations was determined from a 24-item food frequency questionnaire. Neighbourhood environment data were derived from dissemination area level Census data. Two deprivation indices were evaluated: material and social deprivation, which reflect access to goods and amenities and social relationships. Multi-level models were used to estimate relationships (mean differences and 95% CI) between neighbourhood environment and diet quality, adjusting for covariates. Mean diet quality was lower in the most socially deprived neighbourhoods compared to the least socially deprived: −0.56, 95% CI (−0.88, −0.25). Relationships between diet quality and population density differed between urban and rural areas (p-interaction < 0.0001). In rural areas, diet quality was higher in intermediate-density neighbourhoods: 0.54, 95% CI (0.05, 1.03). In urban areas, diet quality was lower in intermediate-density and the most-dense neighbourhoods: −0.84, 95% CI (−1.28, −0.40) and −0.72, 95% CI (−1.20, −0.25). Our findings suggest socially deprived and high-density neighbourhoods are associated with lower diet quality and possible urban-rural differences in neighbourhood environment-diet quality relationships. Additional studies are needed to determine the temporal nature of relationships and whether differences in diet quality are meaningful.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".