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Record W2760870660 · doi:10.1002/ejsp.2343

Gaming motivation and problematic video gaming: The role of needs frustration

2017· article· en· W2760870660 on OpenAlex
Devin J. Mills, Marina Milyavskaya, Nancy L. Heath, Jeffrey L. Derevensky

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Journal of Social Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsMcGill UniversityCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFrustrationPsychologySelf-determination theorySocial psychologyContext (archaeology)Video gameStyle (visual arts)Multimedia

Abstract

fetched live from OpenAlex

Abstract Motivation is often used as a predictor of a problematic style of video game engagement, implying that individuals' gaming undermines optimal functioning. Drawing from recent advances in Self‐Determination Theory (SDT), the present study explores the links between gaming motivations, the daily frustration of basic psychological needs, and reports of problematic video gaming (PVG). A sample of 1029 participants (72.8% male; M = 22.96 years; SD = 4.13 years) completed items regarding their gaming engagement and gaming motivation as well as their experience of needs frustration and PVG symptoms. Results revealed positive associations between gaming motivations and PVG, and between daily needs frustration and PVG. Finally, after comparing several competing models, a mediational model whereby needs frustration explained the association between individuals' gaming motivation and PVG emerged as best fitting the data. The discussion addresses the theoretical and practical implications of these findings in the context of recent research.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.045
GPT teacher head0.329
Teacher spread0.284 · 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