Self-Esteem and Body Appreciation in Students of Sports Sciences Faculty: A Structural Equation Model
Bibliographic record
Abstract
The aim of this research is to examine the body appreciation and self-esteem levels of sports sciences faculty students. In this study, it was used relational screening model that is one of the quantitative reserch methods. The research group of the study was composed of students who study in Faculty of Sports Sciences in Gazi University. In study, Rosenberg Self-Esteem Scale (RSES) and Body Appreciation Scale (BAS) were used as data collection tools. The Pearson Moments Product Correlation, structural equation model and path analysis independent sample t-test, one-way ANOVA, percentage, frequency and descriptive statistics were used in data analysis. As a result of the research, it was determined that the self-esteem of students of sports sciences faculty is at an average level. In addition, it was found that the level of participants’ appreciating their own body was high. Moreover, it was concluded that self-esteem of female participants was higher than male participants, athletes in team sports has higher self-esteem than athletes in individual sports, 40-55 kg participants had a higher body appreciation score than 101 and over kg participants. Finally, it was concluded that the level of body appreciation increases as the level of self-esteem increases. As a conclusion of this study, it can be said that higher body appreciation correlated with higher self-esteem.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".