The Canadian Assessment of Physical Literacy: Development of a Model of Children’s Capacity for a Healthy, Active Lifestyle Through a Delphi Process
Bibliographic record
Abstract
BACKGROUND: The Canadian Assessment of Physical Literacy (CAPL) was conceptualized as a tool to monitor children's physical literacy. The original model (fitness, activity behavior, knowledge, motor skill) required revision and relative weights for calculating/interpreting scores were required. METHODS: Nineteen childhood physical activity/fitness experts completed a 3-round Delphi process. Round 1 was open-ended questions. Subsequent rounds rated statements using a 5-point Likert scale. Recommendations were sought regarding protocol inclusion, relative importance within composite scores and score interpretation. RESULTS: Delphi participant consensus was achieved for 64% (47/73) of statement topics, including a revised conceptual model, specific assessment protocols, the importance of longitudinal tracking, and the relative importance of individual protocols and composite scores. Divergent opinions remained regarding the inclusion of sleep time, assessment/ scoring of the obstacle course assessment of motor skill, and the need for an overall physical literacy classification. CONCLUSIONS: The revised CAPL model (overlapping domains of physical competence, motivation, and knowledge, encompassed by daily behavior) is appropriate for monitoring the physical literacy of children aged 8 to 12 years. Objectively measured domains (daily behavior, physical competence) have higher relative importance. The interpretation of CAPL results should be reevaluated as more data become available.
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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 | 0.080 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".