A Quasi-Experimental Study of the Effects of an Outdoor Learning Program on Physical Activity Patterns of Children with a Migrant Background: the PASE Study
Bibliographic record
Abstract
Introduction: Despite the recognized benefits of physical activity on health, most youth, especially those with a migrant background, do not meet movement guidelines. Outdoor learning is recognized as a promising intervention to address this issue. The objective of this quasi-experimental study was to measure the effects of the PASE (“Outdoors, Health and Environment”) outdoor learning program on the physical activity of students with a migrant background compared to a control group with similar sociocultural characteristics. Methods: In October 2019, 91 participants from six elementary grade 6 classes (47.3% female, age 11.61 ± 0.41) wore a validated accelerometer for 7 consecutive days. Three comparative analyses were performed: full week, school day, and activity domains. The Mann-Whitney U test for independent samples was used to compare the differences in means and Cohen’s d was calculated to obtain their effect sizes. Results: Analysis of the full week revealed no significant differences between groups. Analysis of school days without physical education classes showed that girls exposed to PASE spent a greater percentage of their time in MVPA than those in the control group (+4.30%, 95% CI = 1.93 to 6.68; p < 0.01) with a strong effect size (d = 1.14). In the activity domain analysis, more time in MVPA was spent in PASE outdoor learning than in the regular classroom (+11.15%, 95% CI = 9.70 to 12.61; p < 0.01) with a strong effect size (d = 3.63). Conclusion: Outdoor learning has positive effects on the physical activity of students with a migrant background during school hours. Further studies are needed to confirm these observations.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".