The Relationship Between Self-Regulatory Efficacy and Physical Activity in Adolescents With a Caveat: A Cross-Lag Design Examining Weather
Bibliographic record
Abstract
PURPOSE: The use of self-efficacy to predict physical activity has a long history. However, this relationship is complex, as self-efficacy is thought to influence and be influenced by physical activity. The directionality of the self-regulatory efficacy (SRE) and physical activity relationship was examined using a cross-lagged design. A secondary purpose was to examine these relationships across differing weather conditions. METHODS: Canadian adolescents (N = 337; aged between 13 and 18 years) completed the physical activity and SRE measures 4 times during a school year. Structural equation modeling was used to perform a cross-lag analysis. RESULTS: The relationships between physical activity and SRE appeared to be weather dependent. During a more challenging weather period (eg, cold weather), the relationship between physical activity and SRE was bidirectional. However, no relationship emerged when the 2 constructs were assessed during a more optimal weather period (eg, warm weather). CONCLUSIONS: Some support has been provided for the bidirectional nature of the relationship between physical activity and SRE. The relationship appeared to be qualified by climate considerations, suggesting that future research examine how weather may relate not just to physical activity but also to the correlates of physical activity.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".