Investigating Adolescents’ Video Gaming and Gambling Activities, and Their Relationship With Behavioral, Emotional, and Social Difficulties: Protocol for a Multi-Informant Study
Bibliographic record
Abstract
BACKGROUND: Growing empirical evidence suggests that adolescents have a relatively greater propensity to develop problematic video gaming or gambling habits. OBJECTIVE: The main objectives of this study are to estimate the prevalence of potential pathological gambling and video game use among adolescent students and to evaluate their risk factors. METHODS: This is a cross-sectional multi-informant study based on an online survey. It will include a sample of adolescents attending secondary schools located in Brescia, northern Italy, their schoolteachers, and parents. The survey includes extensive data on adolescents' (1) demographic, social, economic, and environmental characteristics; (2) behavioral, emotional, and social problems and adaptive functioning; (3) emotional and social loneliness; (4) perception of the reasons to use social networks; (5) video game habits and pathological use of video gaming; and (6) gambling behaviors. RESULTS: This protocol was approved by the Institutional Ethics Board of the Spedali Civili of Brescia (Italy). We expect to collect data from 793 or more adolescent students, as determined by our sample size calculation. CONCLUSIONS: This multisite project will make a substantial contribution to (1) the implementation of a system for identifying pathological gambling and pathological video game use among adolescents, allowing for interventions aimed at improving adolescents' financial, emotional, and social well-being; and (2) the identification of distinct profiles of gamblers and pathological video gamers that will contribute to setting up effective targeted prevention measures. Understanding the causes and impact of gambling and pathological video gaming on adolescents is a public health issue. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33376.
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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 | 0.038 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".