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Record W3157780392 · doi:10.5539/ies.v14n5p135

The Relationships Between Internet Addiction, Social Appearance Anxiety and Coping with Stress

2021· article· en· W3157780392 on OpenAlexvenueno aff
Umay Bilge Baltacı, Melike Yılmaz, Zeliha Traş

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyThe InternetAddictionAnxietyCoping (psychology)Social anxietyDescriptive statisticsClinical psychologySocial supportAddictive behaviorSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study is to examine internet addiction in terms of social appearance anxiety and strategies for coping with stress. The dependent variable of the research is internet addiction, and its independent variables are social appearance anxiety and strategies for coping with stress. The study group of the research consists of 481 undergraduate and postgraduate students as 318 women (66.1%) and 163 men (33.9%). In order to collect data in the study, Short Version of Young’s Internet Addiction Test, The Social Appearance Anxiety Scale, The Stress Coping Strategy Scale, and Personal Information Form were used. Descriptive statistics, correlation and multiple regression analysis were used to analyze the data. A positive relationship was found between internet addiction and social appearance anxiety of university students. While there is a positive relationship between submissive approach and helpless approach, which are the subscale of coping strategies, and internet addiction of university students, there is a negative relationship between self-confident approach and optimistic approach. The results of the research revealed that the submissive approach and self-confident approach, which are the subscale in for coping with stress, social appearance anxiety are predictive of internet addiction.

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)0.001
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 score0.352
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.063
GPT teacher head0.391
Teacher spread0.328 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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