Predicting Physical Activity Intention and Behavior in School-Age Children
Bibliographic record
Abstract
Two studies were conducted to predict physical activity in school-aged children. Study 1 tested the utility of an integrated model in predicting physical activity (PA) intention and behavior-the theory of planned behavior (TPB) and self-efficacy theory. Six hundred and forty-five New Zealand children (aged 11-13 years) completed measures corresponding to the integrated model and a self-reported measure of PA one week later. Perceived behavioral control (PBC) and subjective norm were the two strongest predictors of intentions. Task efficacy and barrier efficacy were the two strongest predictors of PA. A second study (Study 2) was conducted to determine whether the self-efficacy measures could discriminate objectively measured PA levels. Sixty-seven Canadian children (aged 11-13 years) completed task and barrier self-efficacy measures. The following week, children classified as 'high' (n = 11) and 'lower' (n = 7) for both task and barrier efficacy wore an Actical® monitor for seven consecutive days to provide activity-related energy expenditure (AEE) data. Results showed that children with high efficacy expended significantly greater AEE than their lower efficacious counterparts. Findings from these two studies provide support for the use of self-efficacy interventions as a potentially useful means of increasing PA levels among school-aged children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".