Contributions of After School Programs to the Development of Fundamental Movement Skills in Children
Bibliographic record
Abstract
International Journal of Exercise Science 7(3) : 236-249, 2014. Fundamental movement skill (FMS) proficiency or the ability to perform basic skills (e.g., throwing, catching and jumping) has been linked to participation in lifelong physical activity. FMS proficiency amongst children has declined in the previous 15 years, with more children performing FMS at a low-mastery level. These declines may help explain the insufficient levels of participation in health promoting physical activity seen in today’s youth. The after school time period (e.g., 3 to 6 p.m.), is increasingly considered an opportune time for physical activity interventions. To date, little research has examined the potential for after school programming to improve FMS proficiency. Participants (n=40, 6-10 years) of two existent physical activity based after school programs, a low-organized games and a sports-based program, were pre- and post-tested for FMS proficiency using the Test of Gross Motor Development-2 (TGMD-2) over an 11-week period. The sports-based program participants showed no improvement in FMS over the 11-week study (p=0.91, eta2=0.00) and the games-based program participants significantly improved their proficiency (p=0.00, eta2=0.30). No significant (p=0.13, eta2 = 0.06), differences were found in change in FMS scores between the low-organized games program participants and the sport-based program participants. These results suggest that after school programs with a low-organized games-based focus may support a moderate improvement in FMS proficiency in young children. Better training of after school program leaders on how to teach FMS may be necessary to assist children in acquiring sufficient proficiency in FMS.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".