Investigation of the Physical Activity Self-Worth of Women Who Study in University
Bibliographic record
Abstract
This study was conducted with the aim of investigating the physical activity self-worth of women who study in university. To measure the physical activity self-worth of the participants, the “Physical Activity Self-Worth Inventory”, which was developed by Huberty et al. (2013), was employed. As a result of the data obtained, “Independent samples t-test”, which is one of the parametric tests, was conducted to evaluate the level of differentiation in the variable of gender, an independent variable. To determine the differences in the answers provided for the question, “Are there any physical activities that you do for at least half an hour or longer in a week?” and the variable of grade, “One-Way Variance Analysis” was conducted, which is one of the parametric tests. The level of statistical significance (α error level) was regarded as p<0.05. According to the faculties where the participants studied, it was determined that there were statistically significant differences in terms of the scores of physical activity knowledge subscale, physical activity emotional subscale and the total scores of physical activity self-worth inventory (p<0.05). According to the participation of the participants in physical activities, it was determined that there were statistically significant differences in terms of the scores of physical activity knowledge subscale, physical activity emotion subscale and the total scores of physical activity self-worth inventory (p<0.05). According to the grade at which the participants studied, it was determined that there were no statistically significant differences in terms of the total scores of physical activity self-worth inventory and all of its subscales (p>0.05). In conclusion, in the comparison of the students of the faculty of sports sciences to the students of other departments, it was observed that the physical activity self-worth scores of those who exercise at least half an hour were higher compared to those who exercise irregularly and those who never exercise. Accordingly, the contents of elective lessons that can increase participation in physical activities in other departments of universities should be increased. To increase the individuals’ time and frequency of participation in physical activities, informative education that includes the benefits of physical activity to general health should be conducted.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".