Fatigue Index, Haemoglobin Level and Physical Fitness: A Correlation Analysis Study
Bibliographic record
Abstract
Physical fitness is one of the most important indicators in daily life, supported by various factors including fatigue index and haemoglobin levels. The purpose of this study was to determine: 1) the contribution of fatigue index to physical fitness, 2) the contribution of haemoglobin level to physical fitness, and 3) the contribution of fatigue index and haemoglobin level simultaneously to physical fitness. The research studied belongs to the type of quantitative research with a correlational approach and continued by calculating the amount of contribution of the independent variable (predictor) to the dependent variable (criterion) through the determination index, namely r2 x 100%. Sampling was carried out using purposive sampling technique and the sample amounted to 48 people. The characteristics of this research sample are active sports students aged 18.68 ± 0.86 years, height 165.38 ± 6.71 centimetres, and body weight 56.75 ± 9.70 kilograms. Fatigue Index was measured by Running Based Anaerobic Sprint Test (RAST Test), Haemoglobin Level was measured by Sahli method, and Physical fitness was measured by the Bleep Test. The data analysis techniques used were simple and multiple correlation analysis techniques. Hypotheses 1 and 2 were analysed by simple correlation and regression, while hypothesis 3 was analysed by correlation and multiple regression. Simple correlation and multiple regression analysis, this analysis is used to determine the contribution of fatigue index variables and haemoglobin levels to physical fitness. The results in this study are: 1) Fatigue index contributes to the level of physical fitness by 6.09%. 2) Haemoglobin levels contribute to the level of physical fitness by 9.14%, and 3) Fatigue index and haemoglobin levels together contribute to the level of physical fitness by 16.83%. Keywords: Fatigue Index, Haemoglobin Level, and Physical Fitness.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".