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Record W3007704617 · doi:10.1016/j.abrep.2020.100267

How maladaptive cognitions contribute to the development of problematic social media use

2020· article· en· W3007704617 on OpenAlexaff
Giulia Fioravanti, Gordon L. Flett, Paul L. Hewitt, Laura Rugai, Silvia Casale

Bibliographic record

VenueAddictive Behaviors Reports · 2020
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversity of British ColumbiaYork University
Fundersnot available
KeywordsPsychologyFeelingPessimismCognitionPerceptionSocial anxietySocial cognitionSocial mediaPreferenceSocial cognitive theorySocial psychologyDevelopmental psychologyClinical psychologyAnxiety

Abstract

fetched live from OpenAlex

The current study investigates the effects of perfectionism discrepancies (PD) and social hopelessness (SH) on problematic social media use as conceptualized by the cognitive-behavioral model. METHODS: A sample of 400 university students (52.3% women; mean age = 22.01 ± 1.99) completed measures assessing PD, SH, and problematic social media use. RESULTS: Structural equation modeling showed that both social hopelessness and feeling discrepant from personal and prescribed standards predicted the preference for online social interactions (POSI). POSI predicted the motivation to use online social media as a means of alleviating distressing feelings, the inability to regulate social media use and the negative outcomes resulting from use of SNS. CONCLUSIONS: In line with the cognitive-behavioral model of problematic Internet use, the present study suggests the primary importance of maladaptive cognitions about the self (i.e. perfectionism discrepancies) and the world (i.e. social hopelessness) for the development of a preference for online social interactions. In particular, the present study shows that individuals are likely to opt for online social interactions as a function of their pessimistic social expectancies and the sense of inadequacy that comes from perceptions of falling short of expectations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.699

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 communication0.0000.000
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 head0.071
GPT teacher head0.307
Teacher spread0.237 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

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