Leisure and Problem Gaming Behaviors Among Children and Adolescents During School Closures Caused by COVID-19 in Hong Kong: Quantitative Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: School closures during the COVID-19 pandemic may have exacerbated students' loneliness, addictive gaming behaviors, and poor mental health. These mental health issues confronting young people are of public concern. OBJECTIVE: This study aimed to examine the associations between loneliness and gaming addiction behaviors among young people in Hong Kong and to investigate how familial factors, psychological distress, and gender differences moderate these relationships. METHODS: This cross-sectional study was conducted in June 2020 when schools reopened after 6 months of school closures. Participants included 2863 children and adolescents in primary (Grades 4 to 6) and secondary (Grades 7 and 8) schools (female participants: 1502/2863, 52.5%). Chi-square tests, one-way analyses of variance, and independent-samples t tests were performed to compare the differences of distribution in gaming addiction behaviors across gender, age, and other sociodemographic characteristics. Multinomial logistic regression analyses were conducted to identify factors that relate to excessive or pathological gaming behaviors separately, in comparison with leisure gaming. RESULTS: A total of 83.0% (2377/2863) of the participants played video games during the COVID-19 pandemic. The prevalence of excessive and pathological game addiction behaviors was 20.9% (597/2863) and 5.3% (153/2863), respectively. More male students had gaming addiction symptoms than female students. The multinomial logistic regressions showed that feeling lonely was associated with more problematic gaming behaviors, and the association was stronger for older female students. Low socioeconomic status, less parental support and less supervision, and poor mental health were risk factors for gaming addiction behaviors, especially among primary school students. CONCLUSIONS: Loneliness was associated with gaming addiction behaviors; the findings from this study suggested that this association was similar across gender and age groups among young people. Familial support and supervision during school closures can protect young people from developing problematic gaming behaviors. Results of this study have implications for prevention and early intervention on behalf of policy makers and game developers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".