Learning Disabilities and Risk-Taking Behavior in Adolescents
Bibliographic record
Abstract
Risk-taking behavior includes alcohol and drug use, delinquency, acts of aggression, sexual activity, and so on. Many studies have explored the relationship between adolescents and risk-taking behavior; however, only a few studies have examined this link in adolescents with learning disabilities (LD) or attention-deficit/hyperactivity disorder (ADHD). The purpose of the present study was to address that limitation by comparing the risk-taking behavior of adolescents with LD (n=230), with comorbid LD/ADHD (n=92), and without LD or ADHD (n=322) on their substance use, engagement in major and minor delinquency, acts of aggression, sexual activity, and gambling activities. The study also investigated whether psychosocial variables (e.g., well-being) may act as mediating variables that help explain between-group differences. Results suggest that it is a combination of the LD and the secondary psychosocial characteristics that explains why adolescents with LD and comorbid LD/ADHD more frequently engage in some risk-taking behavior.
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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 | 0.000 | 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 | 0.000 |
| 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".