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Record W7006397023

Traits associated with internet\n\t\t\t\t addiction in young adults: Potential risk factors

2017· article· en· W7006397023 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2017
Typearticle
Languageen
FieldComputer Science
TopicBig Data and Digital Economy
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionAnxiety sensitivityAnxietyAlexithymiaMoodBig Five personality traitsMediationPersonalitySubstance abuseImpulsivity
DOInot available

Abstract

fetched live from OpenAlex

The present study sought to determine whether certain personality traits associated with problematic substance use may also characterize young adults who report problematic internet use. An index of internet addiction as well as measures of traits previously linked to problematic substance use were administered to a sample of 86 young adults aged 18–30 years. Measures included the Internet Addiction Test (IAT), Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), Depression Anxiety and Stress Scales (DASS-21), Toronto Alexithymia Scale (TAS-20), and the Fear of Intimacy Scale (FIS). Results indicated that IAT scores were significantly positively correlated with TAS-20, DASS-21, SPSRQ and FIS scores, as predicted. When age, gender and negative mood were controlled in a hierarchical regression, sensitivity to punishment (SP), sensitivity to reward (SR) and FIS significantly contributed to variance in IAT in the final model. SP partially mediated the relationship between TAS-20 and IAT, whereas no such mediation was indicated for SR or FIS. Present findings suggest that alexithymia and reward sensitivity may be important risk factors for internet addiction as for problematic substance use, whereas sensitivity to punishment may account for at least part of the association between alexithymia and problematic use of the 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.003
Open science0.0020.000
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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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