The Physical Activity Levels and Sedentary Behaviours of Latino Children in Canada
Bibliographic record
Abstract
Objective: To assess the physical activity and sedentary behaviors of a sample of Latino children in London, Ontario, Canada. Methods: Seventy-four Latino children (54.1% male; mean age = 11.4) completed self-report questionnaires related to physical activity and sedentary behaviors. A subset of children (n = 64) wore Actical (Mini Mitter, Respironics) accelerometers for a maximum of four days. Results: Latino children self-reported moderate levels of physical activity (i.e., mean score of 2.8 on 5-point scale). Accelerometer data revealed that children spent an average of 50.0 min in moderate-to-vigorous physical activity (MVPA; 59.2 min on weekdays and 50.6 min on weekend days) and were sedentary for an average of 8.4 h (508.0 min) per day (533.5 min on weekdays and 497.7 min on weekend days). Children reported spending an average of 3.8 h (228 min) daily in front of screens—1.7 h (102 min) watching television, 1.2 h (72 min) on the computer, and 0.9 h (54 min) playing video games. Conclusions: This feasibility project provided a preliminary account of objectively measured daily physical activity and sedentary time among a sample of Latino children in Canada, as well as insight into the challenge of measuring these behaviors. Sedentary behavior reduction techniques should be explored and implemented in this young population, along with strategies to promote adherence to accelerometer protocols.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".