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Record W4206464000 · doi:10.5539/ies.v15n1p47

Social Appearance Anxiety, Automatic Thoughts, Psychological Well-Being and Social Media Addiction in University Students

2022· article· en· W4206464000 on OpenAlexvenueno aff
Hazal Rümeysa Aslan, Özlem ÇAKMAK TOLAN

Bibliographic record

VenueInternational Education Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAddictionSocial mediaSocial anxietyAddictive behaviorAnxietyMultilevel modelScale (ratio)Regression analysisDevelopmental psychologyClinical psychologySocial psychologyStatisticsPsychiatry

Abstract

fetched live from OpenAlex

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.

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)0.001
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 score0.208
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.036
GPT teacher head0.407
Teacher spread0.371 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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