Examination of the Selected Physical and Motoric Characteristics of Students with Special Needs in Turkish Schools Aged 7–14
Bibliographic record
Abstract
The aim of this paper was to examine the selected physical and motoric characteristics of students with mild intellectual disabilities. The total number of the participants was 119 (54 females and 65 males) and the mean age was 10.78 ± 1.88 years. Height, weight, body mass index (BMI), body fat percentage, and body fat mass scores were collected to determine the physical characteristics. Handgrip strength, vertical jump, standing long jump, flexibility, and 20 m speed running tests were performed to determine the motoric characteristics. The data were analyzed using IBM SPSS 22 package program. Descriptive statistical methods were used in the evaluation of the data. The male students performed better than the female students in all motor performance tests except the flexibility test. The older students performed better, as in the previous studies. Most of the students in the study were found to have a low or normal body mass index. However, according to the literature, children with special needs tend to be overweight and obese due to sedentary lifestyle. One reason for this difference might be a small sample size. Other reasons could be different socio-economic backgrounds and different extracurricular physical activity habits.
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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 | 0.000 | 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".