A park typology in the QUALITY cohort: Implications for physical activity and truncal fat among youth at risk of obesity
Bibliographic record
Abstract
The operationalization of opportunities for physical activity (PA) in parks has not been studied extensively. To explore associations between park types, PA and adiposity in youth. Data were from an ongoing cohort study in children at risk of obesity. Data were collected in 512 participants (2005–2008). Analyses were restricted to 380 participants living within ≥ 1000 m of ≥ 1 park (n parks = 576). Park types were identified using principal component and cluster analyses. Linear and logistic regressions were used to explore associations between park types, and PA and adiposity. The reference category was children living near smaller-sized parks with no team PA features. Nine park types were identified. Compared to the reference group, children living near esthetically pleasing parks with few team sports installations reported more 15-minute bouts of PA/week (bouts of PA) (β = 5.2 [90% CI: 2.3; 8.1]) and variety of PA (1.6 [0.1; 3.1]), and had less % truncal fat (− 3.4 [− 6.4; − 0.5]). Children living near parks that were low on safety items with cycling infrastructure reported more bouts of PA (2.2 [0; 4.3]) and variety of PA (0; 2.2]). Children living near parks with a variety of PA installations reported more bouts of PA (2.5 [0.2; 4.7]) and variety of PA (1.4 [0.2; 2.5]). Children living near parks that had team sports and pool features reported more bouts of PA (2.5 [0.4; 4.7]). No significant associations were found for objectively-measured PA. Parks that emphasize unstructured activities may increase self-reported PA and be associated with less % truncal fat among youth at risk of obesity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".