Latent Profile Analysis of Children’s Active Physical Recreation Patterns in Middle Childhood
Bibliographic record
Abstract
Understanding factors that influence physical activity participation in middle childhood is essential for developing effective interventions. To date, many studies have contributed valuable knowledge on the individual, or person-centered, factors that influence participation, such as motor competence and perceived motor competence. However, there is an increasing body of literature in support of exploring participation through a broader lens, considering additional social ecological factors and their role in participation. Understanding the development of unique combinations of personal and environmental characteristics can shed light on participation patterns over time. Therefore, the aim of this study is to identify clusters of a longitudinal sample of children in grades 2, 3, 4, and 5 (n = 155; 55% girls) based on: motor skills; perceived physical competence; active physical recreation; and with whom and where participation occurs. Latent profile analysis results revealed a range of clusters within each grade, with a 3-cluster solution in grade 2, a 5-cluster solution in grade 3, a 4-cluster solution in grade 4, and a 6-cluster solution in grade 5. An analysis of the clusters revealed increasingly diverse clusters over time, with some clusters demonstrating paths toward engagement or disengagement in active physical recreation. The variation in clusters across grades indicates increasing diversity in personal and environmental factors through middle childhood. Recognizing this diversity can allow for teachers, coaches, and instructors to employ instructional styles to accommodate individuals’ differences and maximize participation in a range of physical activity contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".