Autonomous motivation: school leaders as key drivers of physical activity in the Global Health Program
Bibliographic record
Abstract
Introduction Motivation is one of the main factors that can influence physical activity practice in youth. Schools are ideal settings to provide opportunities to be active. However, few school-based behavioral change interventions have been designed with curriculum-based contents and evaluated in a real-life setting. The Global Health Program (GHP), implemented among 10,000 students in Québec (Canada), aims to promote long-term behavior change through educational strategies guided by school leaders. Therefore, the aim of the present study was to (i) investigate the association between school leaders’ implication and motivation for physical activity in GHP participants, and (ii) to test whether this association is moderated by physical activity level (active vs. inactive) or number of years of participation in the program. Methods A cross-sectional study among children and adolescents participating in the GHP was conducted. The data collection was carried out in the Fall 2024 using self-report online questionnaires. Demographic data, motivation for physical activity, perception of school leaders’ implication and physical activity level were collected. Linear regressions analysis with interaction terms to examine potential moderating effect were used. Results Results showed that among the 658 participants (42% girls, mean ± SD age = 14.5 ± 1.8 years) 29.4% reported being active participants have been involved in GHP for an average of 3.1 ± 2.1 years. There was a positive association between school leaders’ implication and autonomous motivation [ β̂ = 0.26; (95%CI 0.138; 0.256)]. However, neither PA level [ β̂ = − 0.02, (95%CI −0.142; 0.104)] nor years of participation in GHP [ β̂ = − 0.02, (95%CI −0.144; 0.102)] moderated this relationship. Conclusion Results support the importance of the role of school leaders on students’ motivational quality, regardless of their PA status or exposure length to intervention programs. This insight emphasizes the value of cultivating supportive school environments and leadership practices that consistently promote autonomous motivation, thereby encouraging long-term engagement in physical activity among youth, as fostered by the GHP in Québec (Canada).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".