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Record W4399972700 · doi:10.5817/cp2024-3-5

Internalizing personality traits and coping motivations for gaming during the COVID-19 pandemic: A cross-lagged panel mediation analysis

2024· article· en· W4399972700 on OpenAlex
Rebecca Lewinson, Jeffrey D. Wardell, Joel Katz, Matthew T. Keough

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.

Bibliographic record

VenueCyberpsychology Journal of Psychosocial Research on Cyberspace · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMental Health Research CanadaCentre for Addiction and Mental HealthUniversity of TorontoYork University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicCoping (psychology)MediationPsychology2019-20 coronavirus outbreakBig Five personality traitsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PersonalitySocial psychologyClinical psychologyVirologyMedicinePolitical scienceInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Anxiety sensitivity and hopelessness are two traits that have been previously linked to increased gaming problems. Research in the early stages of the COVID-19 pandemic showed that emotionally vulnerable individuals were turning to video games as a means of coping with their distress. However, more research is needed on the long-term and enduring pathways from internalizing traits to time spent gaming during COVID-19, after the lockdowns and preventative measures had been lifted. As such, the current study employs a multi-wave longitudinal study that predicted that those participants who experience high levels of anxiety sensitivity or hopelessness would use gaming as a means to cope with their emotional discomfort, resulting in increased gaming behaviours. A sample of 1,001 American gamers (Mage = 38.43, SD = 12.11, 53.2% female) completed three surveys through Mechanical Turk, with the first occurring in July 2021, and subsequent surveys spaced three months apart. This study measured participants’ baseline anxiety sensitivity and hopelessness using the Substance Use Risk Profile. At each time point, participants were asked to recall their average time spent gaming over the past month using a Timeline Follow-Back method, and answer questions related to their coping motivations for gaming using the Motives for Online Gaming Questionnaire. Coping motives consistently predicted time spent gaming at the next timepoint. Furthermore, we found evidence that high levels of anxiety sensitivity at baseline predicted greater future time spent gaming at Time 3, through greater coping motives at Time 2. Hopelessness was correlated with coping motives and time spent gaming at baseline, but did not relate to these variables across time. Anxious individuals who were gaming to cope during the COVID-19 pandemic may be at higher risk for excessive gaming. This may be particularly true for individuals who are higher in anxiety sensitivity. Future research should aim to understand how the relationships between anxiety sensitivity, coping motivations, and time spend gaming exist in the context of symptoms of gaming disorder and functional impairments that exist due to excessive gaming.

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)0.012
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.218
GPT teacher head0.535
Teacher spread0.317 · 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