Association between obesity in 4‐ to 7‐year‐old children and eight types of crime: a hierarchical linear modelling approach
Bibliographic record
Abstract
OBJECTIVE: Evidence of the association between childhood obesity and neighbourhood crime is inconclusive. Most previous studies have included children of all ages, and few have examined different types of crime. The objective of this study was to investigate the association between obesity and eight different types of crime (i.e. commercial robbery, street robbery, assault, other violence, commercial break and enter, residential break and enter, theft of vehicle and theft from vehicle) among 4- to 7-year-old children in a large western Canadian city. METHODS: = 10,069) using spatial analysis and hierarchical generalized linear modelling. The outcome variable was normal weight or obesity. The exposure variable was the distance between the child's residential postal code and the closest occurrence of each type of crime. RESULTS: Controlling for distance to the closest park, frequency of each type of crime in the child's neighbourhood and neighbourhood factors (proportion of visible minorities, education and median family income), there was no association between any of the crime types and childhood obesity. CONCLUSIONS: Crime did not contribute to obesity in this sample of 4- to 7-year-old children. Replication of this study in other jurisdictions would increase confidence in these results.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".