Is Physical Activity–Related Self-Efficacy Associated with Moderate to Vigorous Physical Activity and Sedentary Behaviour among Ambulatory Children with Cerebral Palsy?
Bibliographic record
Abstract
Purpose : To determine how physical activity–related self-efficacy is associated with physical activity and sedentary behaviour time among ambulatory children with cerebral palsy (CP). Method : Children with CP, Gross Motor Function Classification System (GMFCS) Levels I-III ( N = 26; aged 9–18 y), completed the task self-efficacy component of a self-efficacy scale and wore Actigraph GT3X+ accelerometers for 5 days. Correlations (Pearson and Spearman’s rank-order; α = 0.050) were conducted to evaluate the relationships among age, GMFCS level, self-efficacy, and both daily moderate-to-vigorous physical activity (MVPA) and sedentary time. Linear regression models were used to determine the relationships among the independent variables and MVPA and sedentary time. Results : Self-efficacy was positively associated with MVPA time ( r = 0.428, p = 0.015) and negatively correlated with sedentary time ( r = –0.332, p = 0.049). In our linear regression models, gross motor function (β = –0.462, p = 0.006), age (β = –0.344, p = 0.033), and self-efficacy (β = 0.281, p = 0.080) were associated with MVPA time ( R2 = 0.508), while GMFCS level (β = 0.439, p = 0.003) and age (β = 0.605, p < 0.001) were associated with sedentary time ( R2 = 0.584). Conclusions : This research suggests that self-efficacy, age, and gross motor function are associated with MVPA in children with CP. Additional research is needed to confirm these findings and further explore the influence of self-efficacy on sedentary behaviour.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".