Growing pains? Examining preschool sport programs' developmental objectives
Bibliographic record
Abstract
Preschool aged children are encouraged to accumulate at least three hours of physical activity (PA) daily (CSEP, 2012), yet there are contradictory reports as to how many preschoolers are meeting these recommendations (AHKC, 2010; CFLRI, 2015). Further, 35% of children in this age group are classified as overweight or obese (Shields &Tremblay, 2010). While many experts suggest free play as an optimal means for preschoolers to attain recommended PA levels (ParticipACTION Report Card, 2015), an interesting paradox is that organized preschool sport programs appear to be growing in popularity. The purpose of this study was to examine proposed developmental outcomes of preschooler sport programs in Toronto. Data was collection through two approaches: a) a systematic Google web search resulted in 90 programs of common organized preschooler sport activities (i.e., soccer, baseball, gymnastics, skating, martial arts, Fraser-Thomas & Safai, in press), and b) a systematic search through the Toronto Fun Guide (City of Toronto, 2016). Content analysis results suggest sport programmers claim extensive physical and psychosocial benefits for preschoolers. Preliminary results show some differences according to sport type (e.g., martial arts cite more psychosocial benefits than other sport types), and program type (e.g., greater promotion of benefits by for profit programs than by City of Toronto programs). The disconnect between proposed developmental outcomes and limited supporting research is discussed. Accordingly, future research is recommended to investigate if and how preschoolers may gain developmental benefits through organized sport.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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".