Gender Comparisons of Physical Fitness Indexes in Inner Mongolia Medical Students in China
Bibliographic record
Abstract
INTRODUCTION: The aim of present study was to investigate gender differences in physical fitness indexes in regard to BMI (body mass index) levels among Inner Mongolia medical students in China. METHODS: Data on participant characteristics came from basic information contained in the school database. Physical fitness indexes including BMI, vital capacity index, sidestep test, and standing long jump, were conducted. RESULTS: Female students had a higher rate of normal weight than those of males. The obesity rate of males was 5 times higher compared to females. Compared with male students, female students had a higher pass rate in vital capacity index, sidestep and standing long jump. Females were higher 17% than males in the pass rate of the sidestep test. Males performed better than females in the standing long jump. In both the malnutrition and normal weight group, the pass rate of the 3 physical fitness indexes for both male and female students was higher than obese group. The not pass rate was higher than pass rate both male and female students in the vital capacity index in the obese group. DISCUSSION: Males had a poor physical fitness level compared with females. Male students may be more likely to spend more time using computers and it will cut down the time of participating in physical activities. So, in our university, more attention should pay on physical education, especially for males.
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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 | 0.000 |
| 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".