Factors Affecting Levels of Health-Related Physical Fitness in Secondary School Students in Selangor, Malaysia
Bibliographic record
Abstract
The purpose of this study was to measure health-related fitness of children based on different implementation levels of the physical education program. Another was to determine the effect of anthropometric and social factors on students’ health-related fitness. A total of 918 students’ age 13, 14, and 16 years old were selected from three different implementation levels program. The total score of the checklist questions was used as criteria in classifying implementation levels in Selangor schools. Heights and weights were measured, from which the BMI was calculated. Data concerning students’ family income were collected from school files. Data on student involvement in a variety of PA during and outside of school hours were gathered from information given by students (SKAF questionnaire). Tanner, self-reported assessment was used to estimate students’ stage of maturation. Length was considered as indicator of adolescent growth. While, students’ health-fitness was measured by a battery of health fitness tests. Effectiveness of these factors on students’ health-related fitness was determined by comparing the pre-post-health-fitness tests scores of students. Results indicated that children in the high-implementation-level have better-health fitness performance on both pre-test and post-test measurements than children in the low-implementation level. However, health- fitness performances that reflect significant differences were different among age groups. The older age groups generally performed better on overall fitness tests than did the younger age groups. Several covariates had strong relationships with pre and post-test fitness scores for different age groups such as; height, weight, BMI, maturity status, time spent in PA, race, and family income. Variations of health-related fitness performance between students involved in this study are most likely contributing to the different implementation levels. Thus, a well-programmed and supervised PE program can develop the health status of students at all levels of education
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".