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 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 | 0.004 |
| 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".