The role of physical literacy for mental health
Bibliographic record
Abstract
Physical literacy (PL) has received increasing attention as a potential gateway to lifelong physical activity (PA) participation. Given the well-established health benefits associated with regular PA engagement, PL may be a critical determinant of health via its impact on PA. Only recently has a conceptual framework based on existing evidence that links PL to various health outcomes been put forth (Cairney et al., 2019). The purpose of this study was to examine whether PL influences mental health indirectly through PA. Data were derived from Wave 8 of the Physical Health and Activity Study Team longitudinal project. Children ages 12 to 14 (N = 874; 467 boys) completed measures to assess physical literacy (motor competence, perceived competence, motivation, enjoyment), PA and psychological distress. Structural equation modeling revealed a good fit for the data, ?2/df = 5.59.; CFI = .964; SRMR = .028; RMSEA = .072. Despite evidence of a significant negative bivariate correlation between PA and psychological distress (r = -.12, p < .001), findings revealed competitive mediation in which the dominance of the direct path (Effect = -.37, p < .001) resulted in an unexpectedly positive indirect effect (Effect = .07, p = .01). Although PL did not indirectly affect psychological distress through PA, Cairney et al.'s framework was partially supported as evidenced by PA acting as a suppressor variable that increased the magnitude of the buffering effect PL confers for psychological distress. Moving forward, public health should consider positioning PL as a foundational component within mental health promotion strategies.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".