Single and Multiplayer Video Gamers: Looking at Their Experiences and Psychosocial Well-Being During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has impacted our lives in many different ways. One significant impact on daily life was the increased indoor time due to quarantine measures. Data collected suggests video games have become more popular than ever during these unprecedented times (Epstein, 2020). This study aims to explore the experiences and psychosocial well-being of individuals who played single and multiplayer video games during the pandemic. Data was collected through a questionnaire distributed to multiple online communities and forums from June 28th to July 29th, 2021. The total collected responses were n=260. 132 participants identified themselves as playing mostly single-player video games and 128 identified themselves as playing mostly multiplayer games. The results show during the pandemic individuals spent more time playing both types of video games. Motivations for playing single-player games trended towards decreasing anxiety and stress, and avoiding real life, whereas multiplayer motivations tended to trend towards socialization rather than decreasing stress or anxiety. During the pandemic, 40-50% of single and multiplayer gamers indicated decreased mental health. However, both types of players reported improvement in mental and social well-being while playing video games. More multiplayer gamers reported improved social well-being while playing compared to single-player gamers. The survey respondents tended to report having more positive experiences with single-player and multiplayer video games during the pandemic. Results presented video games as a way for individuals to socialize or decrease stress and anxiety. In addition, the comparison between the two types of gamers revealed that single-player respondents tended to play for relaxation, stress reduction, and perhaps improvement in mental health, while multiplayer gamers play to increase social interaction and improve social well-being. Further research is needed to explore the long-term effects of video games during the pandemic after everyone has returned to a pre-pandemic state.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".