The Local Food Environment of Children in London Ontario: A Methodological Comparison
Bibliographic record
Abstract
The present study examined current methodological approaches to characterize the local food environment around children in London, Ontario, assessing variations in BMI and dietary preferences in relation to the choice of food environment measure. Taking advantage of a unique dataset that collected GPS trajectories of children’s schools and homes for a large sample of children between 11 and 14 years of age, two commonly-used approaches (i.e., network buffers and Euclidean buffers), and two novel measures of activity spaces (i.e., standard deviational ellipses and α-hulls) are used as ‘geographic containers’ (i.e., areal units) to derive food outlet measures. Results showed slight to low agreement in the percent of shared area between the various containers and the α-hulls. Kappa statistics further confirmed the slight to low agreement between the food outlet measures derived from activity space containers and Network and Euclidean buffer containers. There is considerable variation in the maximum number of outlets between the various group comparisons across gender, weight status and reported food outlet visit. In addition, results from logistic models point to consistent evidence of gender differences in dietary and weight outcomes across containers, but did not support an overall clear effect of food environment measures across choice of geographic container.\nWhen assessing the role of local food environment on children’s outcomes, studies should select the appropriate geographic container definition depending on whether the focus is on opportunities (accessibility) or affordances (exposure).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".