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Record W2997414203 · doi:10.1177/1555412019897524

Measuring Problem Online Video Gaming and Its Association With Problem Gambling and Suspected Motivational, Mental Health, and Behavioral Risk Factors in a Sample of University Students

2020· article· en· W2997414203 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGames and Culture · 2020
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyVideo gameMental healthAddictionSample (material)Association (psychology)Behavioral addictionEntertainmentSocial psychologyApplied psychologyMultimediaComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Recently, the issue of problem online video game playing and its potential connection with problem gambling has drawn increased attention. Although conceptually similar to many behavioral addictions, there is still no clear consensus on how to best measure and assess problem video game playing. This study validates one proposed measure of problem video gaming—the Problem Video Game Playing Test (PVGT)—in a Canadian undergraduate university student sample. Multivariate results indicate that problem video gaming is positively associated with the average length of time spent gaming, social alienation, and online gaming motives such as competition, escape, coping, recreation, and socializing; but, contrary to the gateway hypothesis, problem gambling and several of its mental health correlates—depression, anxiety, and stress—are not associated with problem video gaming as measured by the PVGT. Limitations and implications of this analysis are discussed.

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.

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)0.000
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 score0.023
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.078
GPT teacher head0.336
Teacher spread0.258 · 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