Prospective Associations Between Play Environments and Pediatric Obesity
Bibliographic record
Abstract
PURPOSE: To identify school typologies based on the availability of play equipment and installations. We also examined the associations between availability of play items and child adiposity. DESIGN: Secondary analysis of longitudinal data. SETTING: Elementary schools in Montreal, Canada. PARTICIPANTS: We used data from the Quebec Adipose and Lifestyle Investigation in Youth study (QUALITY), an ongoing investigation of the natural history of obesity and type 2 diabetes in Quebec children of Caucasian descent. MEASURES: The presence of play items was assessed in each child's school. A trained nurse directly assessed child anthropometric measurements to derive body mass index and waist circumference. Body fat composition was measured using DEXA Prodigy Bone Densitometer System. ANALYSES: The final analytic sample comprised 512 students clustered in 296 schools (81% response). We used K-cluster analyses to identify school typologies based on the variety of play items on school grounds. Generalized estimation equations were used to estimate associations between school clusters and outcomes. RESULTS: We identified 4 distinct school typologies. Children in schools with the most varied indoor play environments had lower overall body fat, B = -1.26 cm (95% confidence interval [CI], -2.28 to -0.24 cm), and smaller waist circumference, B = -4.42 cm (95% CI, -7.88 to -0.96 cm), compared to children with the least varied indoor play environment. CONCLUSION: Our results suggest that policies regulating the availability of play items in schools may enrich comprehensive school-based obesity prevention strategies. Extending research in this area to diverse populations is warranted.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".