Gross Motor Skills In Adolescents
Bibliographic record
Abstract
Increasingly, there is evidence showing that adolescents have significantly reduced physical activity (PA) time. Participation in PA sports and free play could be hampered by gross motor (GM) skills and they are rarely reported as being potential explanations of in the overall reduce PA participation. GM is defined by the ability to use large muscle groups that coordinate body movements involved in PA such as walking, running, jumping and throwing while maintaining balance. Specific motor skills to achieve these activities are: Limps speed, Agility, Balance, and Coordination. There is an increasing body of literature for GM in Cerebral Palsy, Intellectually Challenge, Cognitive impairment, Multiple Sclerosis in adolescents but little is found in normal adolescent. PURPOSE: The purpose of this study is to describe the Gross Motor Skills in adolescents. : METHODS: 226 adolescents (12 to 18 years old, 122 females; 104 males). One group per level from grade 7 to 12 in a public high school were selected. GM performance was evaluated using the UQAC-UQAM GM skills test battery and from 11 tests, 4 GMS were assessed: Limb Speed; Agility; Coordination and Balance. Mean score are presented for each GM skills by gender and grade. T-Test were performed between grade for all GM.: RESULTS: Significant difference were found between grade 7 and grade 11 for both gender in Arm Limp Speed (Male: 79.3±3.0 vs 92.7±2.3, p0.05; Female: 80.6±2.6 vs 92.2±2.7; p<0.05) and Balance (Male: 29.3±23.0 vs 32.7±22.3, p0.05; Female: 29.9±22.6 vs 35.8±23.3; p<0.05). No significant results between grades were found for Coordination and Agility.: CONCLUSIONS: Static and Dynamic balance significantly improved with age as well as Arm Limp Speed. Leg Limp Speed, Agility (circle, shuttle and slalom run) and coordination (eye-hand and hand foot) did not improved for both gender suggesting that adolescents participating in PA requiring these skills are likely to show reduced sport performance.:
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".