Defining and Measuring Active Play Among Young Children: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Many young children are not meeting the Canadian physical activity guidelines. In an effort to change this, the term active play has been used to promote increased physical activity levels. Among young children, physical activity is typically achieved in the form of active play behavior. The current study aimed to review and synthesize the literature to identify key concepts used to define and describe active play among young children. A secondary objective was to explore the various methods adopted for measuring active play. METHODS: A systematic review was conducted by searching seven online databases for English-language, original research or reports, and were eligible for inclusion if they defined or measured active play among young children (ie, 2 to 6 years). RESULTS: Nine studies provided a definition or description of active play, six measured active play, and 13 included both outcomes. While variability in active play definitions did exist, common themes included: increased energy exerted, rough and tumble, gross motor movement, unstructured, freely chosen, and fun. Alternatively, many researchers described active play as physical activity (n = 13) and the majority of studies used a questionnaire (n = 16) to assess active play among young children. CONCLUSION: Much variability in the types of active play, methods of assessing active play, and locations where active play can transpire were noted in this review. As such, an accepted and consistent definition is necessary, which we provide herein.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".