The relationship between bullying victimization and gambling among adolescents
Bibliographic record
Abstract
Victims of bullying are more likely to exhibit health problems, have declining grades, abuse drugs and alcohol, experience depression and low self-esteem. Although bullying victimization has been associated with a host of negative outcomes, problem gambling is a public health problem that has been neglected in the context of bullying victimization. This research investigated the relationship between high-risk gambling and bullying victimization. Responses about gambling behaviours, risk for problem gambling, and bullying victimization was collected from 7,045 high-school students (mean age 15 years old). Chi-square analyses were used to explore rates of bullying victimization (i.e. physical, verbal, cyber and indirect) based on gambling frequency and risk for gambling problems. Binary logistic regression analyses were conducted separately for male and female frequent gamblers to predict high-risk gambling based on bullying victimization. Results indicated that verbal bullying and the number of gambling activities one participated in the last year predicted high-risk gambling among males. For females, physical bullying and number of gambling activities predicted high-risk gambling. These results contribute to a better understanding of problem gambling and its relationship with various forms of bullying victimization among youth, with gender differences in the types of bullying victimization related to high-risk for 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 | 0.004 |
| 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 | 0.001 |
| 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".