Youth gambling problems : the identification of risk and protective factors
Bibliographic record
Abstract
The present study examined the relationship between several risk and protective variables associated with problem gambling, substance abuse, and other multiple risk-taking activities by adolescents. With the goal of identifying protective factors that prevent youth from escalating from social gambling to serious problem gambling, this research examined the relationship between family cohesion, school connectedness, coping and adaptive behaviours, mentor relationships, achievement motivation, involvement in conventional organizations, and the development of three health-compromising outcomes---youth problem gambling, substance abuse, and involvement in multiple risk-taking behaviours (e.g., smoking, unsafe sexual activity, and reckless driving). The sample consisted of 2,179 students, ages 11 to 19, in the Province of Ontario. Family and school connectedness were associated with decreased involvement in excessive gambling, substance use, and multiple risk-taking activities. Furthermore, an examination of the effect of potential protective factors on a set of risk factors predictive of adolescent problem gambling suggested that family cohesion plays a role in the prediction of probable pathological gamblers and those at risk for developing a gambling problem. These findings were interpreted with respect to their implications for the development and implementation of prevention programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".