Characterization of physical literacy in children with chronic medical conditions compared with healthy controls: a cross-sectional study
Bibliographic record
Abstract
To determine the physical literacy, defined as the capability for a physically active lifestyle, of children with medical conditions compared with healthy peers, this multicenter cross-sectional study recruited children with medical conditions from cardiology, neurology (including concussion), rheumatology, mental health, respirology, oncology, hematology, and rehabilitation (including cerebral palsy) clinics. Participants aged 8–12 years (N = 130; mean age: 10.0 ± 1.44 years; 44% female) were randomly matched to 3 healthy peers from a normative database, based on age, gender, and month of testing. Total physical literacy was assessed by the Canadian Assessment of Physical Literacy, a validated assessment of physical literacy measuring physical competence, daily behaviour, knowledge/understanding, and motivation/confidence. Total physical literacy mean scores (/100) did not differ (t(498) = –0.67; p = 0.44) between participants (61.0 ± 14.2) and matched healthy peers (62.0 ± 10.7). Children with medical conditions had lower mean physical competence scores (/30; –6.5 [–7.44 to –5.51]; p < 0.001) but higher mean motivation/confidence scores (/30; 2.6 [1.67 to 3.63]; p < 0.001). Mean daily behaviour and knowledge/understanding scores did not differ from matches (/30; 1.8 [0.26 to 3.33]; p = 0.02;/10; –0.04 [–0.38 to 0.30]; p = 0.81; respectively). Children with medical conditions are motivated to be physically active but demonstrate impaired movement skills and fitness, suggesting the need for targeted interventions to improve their physical competence. Novelty: Physical literacy in children with diverse chronic medical conditions is similar to healthy peers. Children with medical conditions have lower physical competence than healthy peers, but higher motivation and confidence. Physical competence (motor skill, fitness) interventions, rather than motivation or education, are needed for these youth.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".