Gender Differences and Associated Factors Influencing Problem Gambling in Adolescents in Sweden: Cross-sectional Investigation
Bibliographic record
Abstract
BACKGROUND: Although gambling disorder is traditionally considered an adult phenomenon, the behavior usually begins in childhood or adolescence. OBJECTIVE: The aim of this study was to explore the frequency of problem gambling among Swedish adolescents and the suspected associated factors. METHODS: This study was based on data collected through a public health survey distributed in 2016 to pupils in ninth grade of primary school and in second grade of secondary school in Sweden. Bayesian binomial regression models, with weakly informative priors, were used to examine whether the frequency of the associated factors differed between those with and without problem gambling. RESULTS: Approximately 11.7% (469/4002) of the boys in ninth grade of primary school and 13.9% (472/3407) of the boys in second grade of secondary school were classified as problem gamblers. For girls, the corresponding frequencies were 1.2% (48/4167) and 0.7% (27/3634), respectively. The overall response rate was 77% (9143/11,868) among ninth grade pupils and 73.4% (7949/10,832) among second grade pupils, resulting in a total of 17,092 responses. Problem gambling was associated with poor sleep and having tried smoking, alcohol, and other substances among both boys and girls in ninth grade of primary school and boys in second grade of secondary school. Problem gambling among girls in second grade of secondary school was associated with an increased prevalence of having tried smoking and other substances and an increased prevalence of poor sleep. CONCLUSIONS: Using a large representative sample of Swedish adolescents, we found that problem gambling was robustly associated with a substantially increased prevalence of poor sleep and having tried smoking, alcohol, and other substances among both boys and girls in ninth grade of primary school as well as among boys in second grade of secondary school. Our study adds important information for policy makers pointing at vulnerable groups to be considered in their work to prevent problem gambling.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".