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Record W2979826316 · doi:10.1101/19005967

Problematic Internet Use in Children and Adolescents: Associations with psychiatric disorders and impairment

2019· preprint· en· W2979826316 on OpenAlexaff
Anita Restrepo, Tohar Scheininger, Jon Clucas, Lindsay Alexander, Giovanni Abrahão Salum, Diana Paksarian, Kathleen Merikangas, Michael P. Milham

Bibliographic record

VenuemedRxiv · 2019
Typepreprint
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMcMaster University
FundersNational Institute of Mental HealthChild Mind Institute
KeywordsPsychopathologyPsychiatryComorbidityClinical psychologyMedicineAssociation (psychology)Psychology

Abstract

fetched live from OpenAlex

ABSTRACT Objective Here, we leveraged the ongoing, large-scale Child Mind Institute Healthy Brain Network, a transdiagnostic self-referred, community sample of children and adolescents (ages 5-21), to examine the associations between Problematic Internet Use (PIU) and psychopathology, general impairment, physical health and sleep disturbances. Methods A total sample of 564 (190 female) participants between the ages of 7-15 (mean = 10.80, SD = 2.16), along with their parents/guardians, completed diagnostic interviews with clinicians, answered a myriad of self-report questionnaires, and underwent physical testing as part of the Healthy Brain Network protocol. Results PIU was positively associated with depressive disorders (aOR = 2.34; CI: 1.18-4.56; p = .01), the combined subtype of ADHD (aOR = 1.79; CI: 1.08-2.98; p = .02), greater levels of impairment (Standardized Beta = 4.79; CI: 3.21-6.37; p < .01) and increased sleep disturbances (Standardized Beta = 3.01; CI: 0.58-5.45; p = .02), even when accounting for demographic covariates and psychiatric comorbidity. Conclusion The association between PIU and psychopathology, as well as its impact on impairment and sleep disturbances, highlight the urgent need to gain an understanding of mechanisms in order to inform public health recommendations on internet use in U.S. youth.

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)0.001
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 score0.005
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.009
GPT teacher head0.264
Teacher spread0.254 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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