Social Appearance Anxiety, Automatic Thoughts, Psychological Well-Being and Social Media Addiction in University Students
Bibliographic record
Abstract
This study aimed to determine the relationships between social appearance anxiety, automatic thoughts, psychological well-being and social media addiction and the predictive power of these variables on social media addiction. The sample of the study consists of 440 associate degrees, undergraduate and postgraduate students studying in various universities in Turkey. Demographic Information Form, Social Media Addiction Scale, Automatic Thoughts Scale and Psychological Well-being Scale were used as data collection tools in the study. Independent group t-test, one-way ANOVA, Pearson correlation coefficient and hierarchical regression analysis methods were used for the analysis of the obtained data. As a result of the analysis, it was found that there was a positive correlation between social appearance anxiety, automatic thoughts and social media addiction and a negative correlation between social media addiction and psychological well-being. According to the analysis, it was concluded that automatic thoughts and social appearance anxiety significantly predicted social media addiction, while psychological well-being did not significantly contribute to the model. Findings were discussed in light of the relevant literature.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".