Skill-Based and Planned Active Play Versus Free-Play Effects on Fundamental Movement Skills in Preschoolers
Bibliographic record
Abstract
Fundamental movement skill interventions are important for promoting physical activity, but the optimal intervention model for preschool children remains unclear. We compared two 8-week interventions, a structured skill-station and a planned active play approach, to a free-play control condition on pre- and postintervention fundamental movement skills. We also collected data regarding program attendance and perceived enjoyment. We found a significant interaction effect between intervention type and time. A Tukey honest significant difference analysis supported a positive intervention effect showing a significant difference between both interventions and the free-play control condition. There was a significant between-group difference in group attendance such that mean attendance was higher for both the free-play and planned active play groups relative to the structured skill-based approach. There were no differences in attendance between free-play and planned active play groups, and there were no differences in enjoyment ratings between the two intervention groups. In sum, while both interventions led to improved fundamental movement skills, the active play approach offered several logistical advantages. Although these findings should be replicated, they can guide feasible and sustainable fundamental movement skill programs within day care settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".