Area-Level Social Fragmentation and Walking for Exercise: Cross-Sectional Findings From the Quebec Adipose and Lifestyle Investigation in Youth Study
Bibliographic record
Abstract
OBJECTIVES: We determined whether social fragmentation, which is linked to the concept of anomie (or normlessness), was associated with a decreased likelihood of willingness to walk for exercise. METHODS: Data were collected from mothers and fathers of 630 families participating in the Quebec Adipose and Lifestyle Investigation in Youth Cohort, an ongoing longitudinal study investigating the natural history of obesity and insulin resistance in children. Social fragmentation was defined as the breakdown of social bonds between individuals and their communities. We used log-binomial multiple regression models to estimate the association between social fragmentation and walking for exercise. RESULTS: Higher social fragmentation was associated with a decreased likelihood of walking for exercise among women but not men. Compared with women living in neighborhoods with the lowest social fragmentation scores (first quartile), those living in neighborhoods in the second (relative risk [RR] = 0.91; 95% confidence interval [CI] = 0.78, 1.05), third (RR = 0.83; 95% CI = 0.70, 1.00), and fourth (RR = 0.80; 95% CI = 0.65, 0.99) quartiles were less likely to walk for exercise (P = .02). CONCLUSIONS: Social fragmentation is associated with reduced walking among women. Increasing neighborhood stability may increase walking behavior, especially among women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".