Calculating and Comparing Children of Contrasting Socioeconomic Status’ Accessibility to Food Environments Using Activity-Travel Data
Bibliographic record
Abstract
Childhood overweight and obesity rates in Canada are at concerning levels, more apparently so in individuals from lower socioeconomic classes. Patterns of food consumption, a contributor to body weight, are likely influenced by accessibility and availability of food services in children’s neighbourhoods. Previous studies have looked at children’s accessibility to food stores in relation to residential neighbourhoods or to schools. These studies, however, do not account for other urban opportunities children may encounter while travelling throughout the city. This study compares the food environments located within the activity spaces of children with contrasting socioeconomic status (SES). By using activity spaces as the spatial unit of analysis, this research answers the call for food environment assessments that consider individuals’ movement. The activity spaces are measured on a geographic information system using a modified version of a road network buffer and the shortest path network estimation method. Results show that while children participate in similar numbers of activities regardless of SES, children of higher SES traveled more of their environment than low SES children. Low SES children had higher densities of fast food outlets and supermarkets in their activity spaces than high SES children. This higher density of fast food outlets in low SES children’s activity spaces may contribute to the higher prevalence of overweight and obese body mass index (BMI) levels observed in this demographic. The activity space technique used in this analysis has the potential to be used in future studies as a transportation network based measure of accessibility that accounts for mobility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".