Online Gambling Patterns and Predictors of Problem Gambling Among Korean Adolescents During the COVID-19 Pandemic: A Cross-sectional Study
Bibliographic record
Abstract
PURPOSE: This study examined online gambling patterns among Korean adolescents during the COVID-19 pandemic and identified predictors of problem gambling based on a socio-ecological model. METHODS: It used nationally representative data from the 2020 National Survey on Youth Gambling Problems conducted by the Korea Center on Gambling Problems. This study selected a sample of 780 adolescents aged 13-18 years who reported having gambled online at least once in the last three months from the raw data of respondents. They were classified as the non-problem group and problem group according to the Gambling Problems Severity Scale (GPSS) of the Canadian Adolescent Gambling Inventory (CAGI). The predictive factors of problem gambling were analyzed by logistic regression analysis. RESULTS: The prevalence of problem gambling was 24.6 %. Its predictors included intrapersonal [male (odds ratios, OR = 1.67); gambling prior to COVID-19 (OR = 2.08)] and interpersonal factors [frequent gamblers in peers (OR = 4.34); peer pressure (OR = 2.34)]. Social factors, such as gambling in online community (OR = 5.60), sports betting (OR = 53.24), and lotteries (OR = 17.03) were associated with problem gambling. CONCLUSIONS: The major predictors of problem gambling among adolescent online gamblers included peer gambling and specific types of gambling. To prevent problem gambling, strategies targeting peer groups are essential. In addition, nurses need to share with families, schools, communities, and policymakers that online gambling, such as lotteries and sports betting, are high-risk of adolescent problem gambling, and recommend them to collaborate for stricter regulatory measures.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".