Gaming Activity and Possible Changes in Gaming Behavior Among Young People During the COVID-19 Pandemic: Cross-sectional Online Survey Study
Bibliographic record
Abstract
BACKGROUND: Young people's daily lives and social interactions changed remarkably during the COVID-19 pandemic as schools and cinemas closed, leisure activities were cancelled, and gatherings were regulated. Questions have been raised by the media, schools, policy makers, and research communities about the effect on young people's online behaviors. OBJECTIVE: This cross-sectional study aimed to study self-reported changes in gaming, focusing on a younger section of the population during the COVID-19 pandemic in Sweden. We also wanted to look at potential risk factors behind problematic gaming during the pandemic, including gaming patterns, gambling behavior, psychological distress, certain sociodemographic characteristics, health factors, and school situation. METHODS: This was an anonymous online survey study of web panel participants in Sweden (n=1501) to study changes in gaming behaviors during the COVID-19 pandemic. Self-reported increases in gaming were analyzed in logistic regression analyses against sociodemographic and health factors. RESULTS: Within the study population that reported changes in gaming activity, we found significant differences in age, employment status, disposable income, whether they ever played on loot boxes, time spent at home, school attendance, psychological distress, and gambling and gaming problems, as well as significant differences in changes in alcohol consumption and exercise habits. When examining the 16-24-year-old age group who reported changes in gaming activity, we found significant differences within the group in disposable income, time at home, and school attendance. When examining the 25-39-year-old age group who reported changes in gaming activity, we found significant differences within the group in employment status, disposable income, time spent at home, whether the respondents were studying, school attendance level, psychological distress, and gaming problems, as well as significant differences in changes in alcohol consumption and exercise habits. Psychological distress (all age groups analyzed together; 25-39-year-old age group), drinking less alcohol (all age groups analyzed together), spending more time at home (all age groups analyzed together), gaming problems, and exercising less (25-39-year-old age group) were positively correlated with a self-reported increase in gaming activity. Being employed (25-39-year-old age group) and being over 40 years of age (all age groups analyzed together) were negatively correlated with increased gaming. We found no significant correlations in the 16-24-year-old age group. CONCLUSIONS: Those who reported increased gaming during the COVID-19 pandemic were more likely to be 16 years to 39 years old. In the age group of 25 years to 39 years old, the increase was associated with psychological distress, reporting less exercise, and being unemployed. COVID-19 may present as a risk factor of increased online gaming in a small but vulnerable group. More research and preferably longitudinal studies are needed in the field of gaming and effects of the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".