The Predictors of Self-Esteem in University Students: Intolerance of Uncertainty and Alexithymia
Bibliographic record
Abstract
Self-esteem is characterized by self-evaluation and affects the social, emotional, and academic aspects of life. In this respect, high self-esteem is important for human mental health. This research aimed to determine university students' intolerance of uncertainty and alexithymia levels to predict their self-esteem. The research group comprised 365 undergraduate students. The data were collected using the “Rosenberg Self-Esteem Scale (RSE)," "Toronto Alexithymia Scale (TAS)," and “Intolerance of Uncertainty Scale (IUS)." The analysis results of this study, in which the predictive correlational design was employed, revealed that self-esteem decreased as the level of alexithymia and intolerance of uncertainty increased. Besides, alexithymia and intolerance of uncertainty were significant predictors of self-esteem. It is hypothesized that interventions to increase self-esteem will reduce levels of alexithymia and intolerance of uncertainty. The integration of findings regarding alexithymia and intolerance of uncertainty, which are more common in clinical samples, into education will improve academic achievement and welfare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".