Comparison of Physiotherapy and Rehabilitation Department Students in terms of Physical Activity Levels, Balance Levels and Muscle Endurance Values
Bibliographic record
Abstract
Aim: This study aims to compare the physical activity levels, balance levels and muscular endurance values of Physiotherapy and Rehabilitation students among grades. Thus, it will be determined to what extent students apply the knowledge they learned in the courses to their own lives. Method: For the aim of our study, balance and trunk endurance, which are physical fitness parameters related to health, and physical activity levels were examined. Snowball randomization method was used in our study. 36 students (15 male, 21 female) volunteered to participate in our study. Y Balance Test was used for dynamic balance assessment, the Flamingo Balance Test for static balance assessment, the McGill Endurance Tests for endurance assessment, and the International Physical Activity Questionnaire for physical activity levels. SPSS 24.0 program was used in statistical analysis of data and significance value was accepted as p<0.05. Results: There was no difference between groups of students included in the study in terms of age, body mass index and gender parameters (p>0.05). There was no statistically significant difference between groups in dynamic balance, static balance, all directions of endurance and physical activity levels (p>0.05). When the relationship between students' physical activity levels and balance–endurance values was examined, no significant relationship was found in any parameter (p>0.05) except for left-sided endurance (p<0.05). Conclusion: As a result of our study, it was seen that there was no difference between degrees in terms of dynamic balance, static balance, endurance and physical activity levels of Physiotherapy and Rehabilitation students. When physical activity results were examined, it was seen that there were very few students in the inactive group. Based on this, we can say that Physiotherapy and Rehabilitation students are successful in applying the knowledge they learned in lectures in their own lives. We recommend increasing the number of participants in future studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".