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Record W3093923975 · doi:10.4309/jgi.2021.46.7

Facing Life Problems Through the Internet. The Link Between Psychosocial Malaise and Problematic Internet Use in an Adolescent Sample.

2020· article· en· W3093923975 on OpenAlexvenueno aff
Claudia Venuleo, Lucrezia Ferrante, Simone Rollo

Bibliographic record

VenueJournal of Gambling Issues · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessPsychosocialPsychologyMalaiseThe InternetAnxietyAffect (linguistics)Logistic regressionClinical psychologySocial psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Scholars have highlighted the role of negative affect as key correlates of Problematic Internet Use (PIU). According to the assumption that Internet-related behaviours can be seen as mechanisms to cope with everyday life (Kardefelt-Winther, 2017), the present study aims to explore the relation between PIU and psychosocial malaise, expecting that adolescents with high levels of social anxiety, negative emotions, and loneliness are more likely to be associated to the problem group of Internet users. Measures of PIU (GPIUS–2), social anxiety (IAS), negative affectivity (PANAS), and loneliness (ILS) were detected in a sample of 766 students attending Year 9–11 (13–19 years old; 47% females) of public high schools in the territory of Lecce (Apulia–Italy). A sub-group of problematic Internet users was identified (n = 185) and a control group was selected (n = 187). A logistic regression was applied to estimate the effect of psychosocial variables on the differentiation between problematic and control Internet users. Results of the present cross-sectional study show that a higher level of social anxiety, negative emotions, and loneliness increases the probability of belonging to the group of problematic Internet users. No significant differences between males and females were found in GPIU levels. The findings show that, for a better understanding of PIU onset and maintenance among adolescents, it is important, to take into account the life problems which may lead young people to overindulge in Internet use.RésuméLes scientifiques ont mis en lumière le rôle de l’affect négatif comme corrélat significatif de la dépendance. Partant de l’hypothèse que les comportements dans Internet peuvent être vus comme des mécanismes d’adaptation à la vie quotidienne (Kardefelt-Winther, 2017), notre étude visait à explorer la relation entre la cyberdépendance et le malaise psychosocial. On s’attendait à ce que les adolescents affichant un degré élevé d’anxiété sociale, d’émotions négatives et de solitude fassent partie du groupe d’internautes à problème. Des indicateurs de la cyberdépendance (GPIUS-2), de l’anxiété sociale (IAS), de l’affect négatif (PANAS) et de la solitude (ILS) ont été relevés dans un échantillon de 766 élèves de la 9e à la 11e année (13 à 19 ans; 47 % de filles) choisi dans des écoles secondaires publiques du territoire de Lecce (Apulia, Italie). Un sous-groupe d’internautes cyberdépendants a été défini (n=185) et un groupe contrôle sélectionné (n=187). Un modèle de régression logistique a été appliqué en vue d’estimer l’effet des variables psychosociales sur la différenciation entre joueurs cyberdépendants et joueurs du groupe contrôle. Les résultats de l’étude transversale montrent qu’un degré plus élevé d’anxiété sociale, d’émotions négatives et de solitude augmentait la probabilité d’appartenir au groupe d’internautes cyberdépendants. Aucune différence notable n’a été constatée entre les hommes et les femmes quant au degré de cyberdépendance. Les résultats indiquent qu’une compréhension plus fine du développement de la cyberdépendance et de sa persistance chez les adolescents devra tenir compte des problèmes vécus dans leur vie personnelle qui les inciteraient à un usage excessif d’Internet.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.172
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.224
GPT teacher head0.399
Teacher spread0.175 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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