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Record W1730940283

Stuck on screens: patterns of computer and gaming station use in youth seen in a psychiatric clinic.

2011· article· en· W1730940283 on OpenAlexaff
S. Baer, Elliot Bogusz, David A. Green

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsAddictionFunctional impairmentPsychologyPsychiatryPopulationMultivariate analysisClinical psychologyMedicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Computer and gaming-station use has become entrenched in the culture of our youth. Parents of children with psychiatric disorders report concerns about overuse, but research in this area is limited. The goal of this study is to evaluate computer/gaming-station use in adolescents in a psychiatric clinic population and to examine the relationship between use and functional impairment. METHOD: 102 adolescents, ages 11-17, from out-patient psychiatric clinics participated. Amount of computer/gaming-station use, type of use (gaming or non-gaming), and presence of addictive features were ascertained along with emotional/functional impairment. Multivariate linear regression was used to examine correlations between patterns of use and impairment. RESULTS: Mean screen time was 6.7±4.2 hrs/day. Presence of addictive features was positively correlated with emotional/functional impairment. Time spent on computer/gaming-station use was not correlated overall with impairment after controlling for addictive features, but non-gaming time was positively correlated with risky behavior in boys. CONCLUSIONS: Youth with psychiatric disorders are spending much of their leisure time on the computer/gaming-station and a substantial subset show addictive features of use which is associated with impairment. Further research to develop measures and to evaluate risk is needed to identify the impact of this problem.

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 score0.067
Threshold uncertainty score0.999

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 integrity0.0000.000
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 head0.100
GPT teacher head0.301
Teacher spread0.200 · 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

Citations41
Published2011
Admission routes1
Has abstractyes

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