Comparing Muscular Fitness Among School Children Based on Sport Participation and Gender
Bibliographic record
Abstract
The objective of this study was to examine muscular fitness using measurements of the right hand grip (RHG) and left hand grip (LHG), as well as the counter movement jump (CMJ) performance of both athlete and non-athlete school children. Additionally, the study aimed to compare these variables between genders. A total of 221 school children aged 11–13 participated in this study. The participants were categorized into four groups based on their characteristics: female athletes (n = 57), female non-athletes (n = 60), male athletes (n = 59), and male non-athletes (n = 45). The primary exclusion criteria included any musculoskeletal limitations that could potentially affect test performance. The strength of the RHG and LHG (kg) was assessed using the Takei-TKK-5101 device. CMJ heights (cm) were measured using the Smart-Speed device. For all variables, a mixed-design two-way univariate analysis of variance (ANOVA) was employed to identify differences between and within groups. The findings of the present study indicated that the RHG, LHG, and CMJ values of athletes were significantly greater than those of non-athletes for both genders (p < 0.001). Moreover, the analyzed performance values exhibited significant differences between male athletes and male as well as female non-athletes, and also between female athletes and female as well as male non-athletes (p < 0.001). Engaging in sports is highly important for school children aged 11–13 to enhance their muscular fitness. It can be stated that the development of muscular fitness for both genders would be higher in children who participate in sports compared to those who do not engage in sports.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".