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Record W4385461900 · doi:10.52380/ijpes.2023.10.3.1209

The Predictors of Self-Esteem in University Students: Intolerance of Uncertainty and Alexithymia

2023· article· en· W4385461900 on OpenAlexaboutno aff
Aslı Kartol

Bibliographic record

VenueInternational Journal of Psychology and Educational Studies · 2023
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaSelf-esteemPsychologyToronto Alexithymia ScaleClinical psychologyPsychological interventionScale (ratio)Psychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.376
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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