A network approach to understanding obesogenic environments for children in Pennsylvania
Bibliographic record
Abstract
Abstract Network methods have been applied to obesity to map connections between obesity-related genes, model biological feedback mechanisms and potential interventions, and to understand the spread of obesity through social networks. However, network methods have not been applied to understanding the obesogenic environment. Here, we created a network of 32 features of communities hypothesized to be related to obesity. Data from an existing study of determinants of obesity among 1,288 communities in Pennsylvania were used. Spearman correlation coefficients were used to describe the bivariate association between each pair of features. These correlations were used to create a network in which the nodes are community features and weighted edges are the strength of the correlations among those nodes. Modules of clustered features were identified using the walktrap method. This network was plotted, and then examined separately for communities stratified by quartiles of child obesity prevalence. We also examined the relationship between measures of network centrality and child obesity prevalence. The overall structure of the network suggests that environmental features geographically co-occur, and features of the environment that were more highly correlated with body mass index were more central to the network. Three clusters were identified: a crime-related cluster, a food-environment and land use-related cluster, and a physical activity-related cluster. The structure of connections between features of the environment differed between communities with the highest and lowest burden of childhood obesity, and a higher degree of average correlation was observed in the heaviest communities. Network methods may help to explicate the concept of the obesogenic environment, and ultimately to illuminate features of the environment that may serve as levers of community-level intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".