Bibliographic record
Abstract
Physical inactivity contributes to a myriad of poor health conditions. Physical inactivity in childhood predicts inactivity throughout the lifespan. As a result it is important to foster participation in physical activity during childhood. The current study examined the relationship of confidence, motivation, and physical literacy to daily physical activity levels on weekends and weekdays. Participants were 86 boys and girls, grades 4-7, Mage = 10.28 years; SD = 1.20. Confidence and motivation were assessed via an online version of the Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity (Hay, 1992), physical literacy was assessed using the Canadian Assessment of Physical Literacy as per Longmuir (2013), and pedometer step counts were used to measure daily physical activity levels. Results indicated a significant difference between number of steps taken on weekdays (M = 14600; SD = 10124) compared to weekends (M = 10996; SD = 7304); the children took significantly more steps on weekdays than they did on weekend days; t(85) = -3.02, p = .003. Motivation and confidence were not significant predictors of number of daily steps. Physical literacy score (with pedometer steps component score removed) did not predict number of daily steps on weekends or weekdays. Previous research has established a link between parent involvement and child participation in physical activity; school age children typically spend weekends with their parents. Given these relationships interventions to increase parent involvement and opportunities for physical activity on weekends should be emphasized. Acknowledgments: Research funding was provided by the Children's Hospital of Eastern Ontario Research Institute through the RBC Learn to Play project, delivered in partnership with ParticipACTION and the Public Health Agency of Canada
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".