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Record W2966390119 · doi:10.5539/ies.v12n8p1

Role of Problematic Internet Use, Sense of Belonging and Social Appearance Anxiety in Facebook Use Intensity of University Students

2019· article· en· W2966390119 on OpenAlexvenueno aff
Zeliha Traş, Kemal Öztemel, Umay Bilge Baltacı

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsBelongingnessPsychologyScale (ratio)The InternetAnxietySocial anxietySocial psychologySocial mediaRegression analysisDevelopmental psychologyClinical psychologyStatistics

Abstract

fetched live from OpenAlex

The aim of this study was to examine the role of problematic internet usa, sense of belonging and social appearance anxiety in facebook use intensity of university students. The sample of the study consisted of 484 (332 female, 68.6% and 152 male 31.4%) different faculties of various universities of Konya. Problematic Internet Use Scale, General Belongingness Scale, Facebook Intensity Scale, The Social Appearance Anxiety Scale, Personal Knowledge Form were used in the study. Pearson Correlation Product Conduct and multiple regression analysis were used to analyze the data. According to the findings of the study, There was a significant positive relationship between the mean scores of the participants on the Facebook Intensity Scale and the mean scores of the Problematic Internet Use Scale and the Social Appearance Anxiety Scale. Regression analysis examined shows that social appearance anxiety and problematic internet use and general belonging sense average scale scores were significantly prediction of Facebook İntensity Scale Scores. The results of the research were discussed within the framework of the literature. Based on the findings of the research, comments and suggestions were developed.

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 score0.034
Threshold uncertainty score0.285

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 head0.029
GPT teacher head0.344
Teacher spread0.315 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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