Gaming as a coping strategy during the COVID-19 pandemic
Bibliographic record
Abstract
Early in the COVID-19 pandemic, social interactions were constrained by physical distancing guidelines. Consequently, some individuals may have turned to video games to cope with isolation and negative emotions. Previous studies have shown that people who struggle with anxiety and depression are at particular risk for developing problem gaming behaviours. However, there is a paucity of longitudinal research testing pathways from negative emotionality to problem gaming behaviours, especially during the COVID-19 pandemic. Accordingly, we conducted a multi-wave longitudinal study and predicted that high levels of emotional vulnerability (anxiety and depression) in the first month of the pandemic would prospectively relate to elevated time spent gaming and related problems six months later. We also predicted that elevated coping motives for gaming would mediate these associations. A sample of 332 Canadian gamers (Mage = 33.79; 60.8% men) completed three surveys on Prolific, with the first occurring in April 2020 (one-month after the declared COVID-19 state of emergency) and subsequent surveys were spaced three months apart. High initial levels of emotional vulnerability predicted excessive time spent gaming, as well as related problems, six months into the pandemic. Elevated coping motives for gaming uniquely mediated these pathways. This longitudinal study is the first to show that negative emotionality was a vulnerability factor for coping-related problem gaming during the COVID-19 pandemic. As we continue to cope with the longer-lasting impacts of the pandemic, it will be important for individuals who struggle with mood and anxiety issues to find more effective ways of coping.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".