Internalizing personality traits and coping motivations for gaming during the COVID-19 pandemic: A cross-lagged panel mediation analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".