Physical Literacy, Physical Activity, and Health: A Citation Content Analysis and Narrative Review
Bibliographic record
Abstract
Physical literacy has received increased research attention over the last decade focusing on the unification of the definition, measurement, and application, including in school and health-based contexts. In 2019, Cairney et al. released a model positioning physical literacy holistically as a primary determinant of health and disease, mediated by physical activity (PA), the physiological and psychological adaptations associated with PA, and the individual and social/environmental/contextual factors or conditions that impact PA-related behaviour, which had a significant impact on physical literacy-related literature. To assess the impact of the model on the extant literature, and better understand the relationship between physical literacy, PA and health as proposed by Cairney et al., we conducted a citation content analysis and narrative review. 956 citations were identified citing the model proposed by Cairney et al. Of these, 16 used the model to construct a theoretical framework and were included in the extended analysis. Thirteen studies were observational, and participants were all children or young people with a total age range 4-20 years. Results demonstrate that physical literacy is related to health-related fitness variables including aerobic fitness, body composition, flexibility, and muscular strength and power; total PA and MVPA; and health literacy, and wellbeing, supporting the model proposed by Cairney et al. However, gaps remain in understanding critical components of the model (e.g., the proposed mediation pathways), and in clarifying the nature of the relationships in a variety of populations (e.g., clinical populations) and across the lifespan. A pragmatic approach to addressing these gaps is recommended.
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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.038 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".