Modeling Frequency and Duration of Out-of-Home Participation in Physical Activity by School-Age Children
Bibliographic record
Abstract
This paper investigates the frequency and the duration of participation in physical activity by children who go to school in the city of Dhaka, Bangladesh. The study uses data from a 2012 activity-based travel diary survey of 245 students in Grades 6 to 10 in the Dhaka metropolitan area. To explore the trade-offs between factors that affect the frequency of out-of-home physical activity, including the use of active transportation, this study used a zero-inflated Poisson (ZIP) regression model. Additionally, parametric hazard models were estimated to examine the duration of physical activities. The results of the ZIP count model suggest that personal and household characteristics, attributes of the built environment, and time commitments for other mandatory and discretionary activities influence the frequency of participation in physical activity. For example, an increase in the number of children in the household and the presence of sidewalks increased the frequency of out-of home, out-of-school physical activity if everything else was held equal. In contrast, travel time to school, the presence of traffic intersections, and the time commitment for tutoring sessions negatively affected participation in physical activity. In the modeling of the duration of out-of-home physical activity, this study found that a Weibull parametric hazard model outperformed a log logistic model. The duration was influenced by sociodemographic characteristics, spatial context, and escort arrangements. For instance, car ownership and a parental escort increased the duration of physical activity. The paper offers an in-depth behavioral understanding of children's physical activities in a developing country.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".