Examining individual, interpersonal, and environmental influences on children’s physical activity levels
Bibliographic record
Abstract
The purpose of this study was to explore individual-level socio-demographic factors and interpersonal-level factors related to social support, as well as the potential role of neighborhood and school environments that may influence the physical activity (PA) levels of children (ages 9-11). Child and parent questionnaires included individual and interpersonal factors, and PA behaviour. Home postal codes were used to determine the neighbourhood the child resides within, as well as their geographic accessibility to recreation opportunities. The models were assessed using a series of cross-classified random-intercept multi-level regression models as children's PA may be affected by both the school they attend and the neighbourhood in which they live. In the unadjusted model, PA varied significantly across school environments (γ = 0.023; CI: 0.003-0.043), but not across neighbourhoods (γ = 0.007; CI: -0.008 to 0.021). Boys were found to be more active compared to girls (b = 0.183; CI: 0.092-0.275), while the level of PA was lower for children whose fathers achieved post-secondary education (b = - 0.197; CI: -0.376 to 0.018) than for those whose parents completed only high school. The addition of the individual-level correlates did not have a substantial effect on level 2 variances and the level 2 variance associated with school environment remained statistically significant. At the interpersonal level, children's perception of parental support (b = 0.117; CI: 0.091-0.143) and peer support (b = 0.111; CI: 0.079-0.142) were positively related to PA. The level 2 variance for the school environment became statistically non-significant when the interpersonal factors were added to the model. At the environmental level, geographic accessibility did not have a significant association with PA and they did not significantly affect level 1 or 2 variance. As many children do not accrue sufficient levels of PA, identifying modifiable determinants is necessary to develop effective strategies to increase PA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".