Youth Gambling in Croatia - Frequency of Gambling and the Occurrence of Problem Gambling
Bibliographic record
Abstract
The main aim of this study was to explore the frequency of gambling and gambling-related problems among Croatian high school students. The specific objectives have been to explore gender differences, and differences in the frequency and severity of gambling problems regarding grade/age and type of school program. The study included n=2.702 high school students from all for grades and all three types of high school programs (3- and 4-year vocational/professional schools, and grammar schools) from 7 cities (Zagreb, Osijek, Rijeka, Split, Vinkovci, Slavonski Brod and Koprivnica) with equal representation of boys (n=1.330, 49.2%) and girls (n=1.372, 50.8%). The respondents’ mean age was Mage = 16.51 (SDage=1.17). The following instruments were used: Questionnaire on general socio-demographic data, Gambling activities questionnaire (Ricijaš, Dodig, Huić, & Kranželić, 2011) and the Canadian Adolescent Gambling Inventory - CAGI (Tremblay, Stinchfield, Wiebe, & Wynne, 2010). Results show that the lifetime prevalence of gambling among Croatian high-school students is 72.9%. The most prevalent games of chance are sports betting and lottery games, with sports betting being the most frequent of these activities. As much as 12.9% adolescents have already developed serious adverse gambling related consequences. Boys have significantly higher problem gambling rates than girls, while the effects of differences regarding the type of school and grade/age are relatively low. The results provide important baseline data for future research, interventions design, and for the improvement of social policy and legislation.
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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 | 0.002 | 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 | 0.001 |
| 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".