Physical Literacy and Resilience in Children and Youth
Bibliographic record
Abstract
Background There is growing interest in the relationship between physical and psychosocial factors related to resilience to better understand the antecedents of health and successful adaptation to challenges in and out of school, and across the lifespan. To further this understanding, a trans-disciplinary approach was used to investigate the association between the multidimensional constructs of physical literacy and resilience in children at a key stage in their development. Methods Cross-sectional data were collected from 227 school children aged 9-12 years old from five schools in [blinded]. Resilience was measured using the Child and Youth Resilience Measure, and physical literacy through the Physical Literacy Assessment for Youth tools. Data were provided by self-report, surrogate assessors of the child (physical education teachers and parents), and trained assessors for movement skills. These data were analysed using correlation and logistic regression. Results Resilience was significantly correlated with numerous indicators of physical literacy, including movement capacity, confidence, and competence, environmental engagement, and overall perceptions of physical literacy. Regressions indicated that resilience could be predicted by movement confidence and competence, environmental engagement, and overall physical literacy. Conclusions The findings of this study, using a constellation of sources, provide foundational evidence for the link between resilience and physical literacy among children, encouraging the importance of physical literacy development in schools. Longitudinal studies are required to further examine this relationship and how these previously unrelated fields may work together for a richer understanding of the interplay between the physical and psychological determinants of well-being.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".