Common Sports Injuries among Physical Activities Practitioners at the Physical Fitness Centers in Jordan (Comparative Study)
Bibliographic record
Abstract
The aim of the study was to investigate the common sports injuries among physical activities practitioners at the physical fitness centers in Jordan. Study sample consisted of (272) volunteered male (n=221) and female (n=51) (age 30±3). Researchers used a special form used to evaluate athletic injuries. After collecting and analyzing the data. Results showed that the most common sports injuries among sample of the study was muscular tears 27.7%, muscle spasm 20.7%, and tears ligament 20.2%. And the most exposed parts of the body to injury is the lumbar area 26.8%, elbows 16.9%, followed by shoulders 8.9%. Also the study results revealed that the most cusses of injuries was over training 24.14%. Poor warm-up 22.1% and bad technic 11.3%. Bodies-building was the most type of activities subjects to injury with 18.8%. Physical Fitness 6.6% and weight loss 27.7. Results also showed that physical therapy was the most means of treating injuries 54.14%, drugs therapy 33.3% and surgical intervention 4.2%. Also the study shows that males are more exposed to injuries than females. Conclusions: These finding indicate that sports injures is part of physical Activities participations, preventive measures should be taken by participant's the researchers recommended the need for physical and medical checkup before participation in physical activity at the physical fitness centers.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".