Traumatic brain injuries and problem gambling in youth: Evidence from a population-based study of secondary students in Ontario, Canada
Bibliographic record
Abstract
Traumatic brain injury (TBI) is characterized by a change in brain function after an external force or sudden movement to the head. TBI is associated with risk-taking, impulsivity, psychological distress, substance abuse, and violent crime. Previous studies have also linked problem gambling to TBI, but these studies have not controlled for possible confounding variables such as mental health problems and hazardous drinking which are also linked to TBI. This study examines the relationship between problem gambling and TBI among adolescents. Data were obtained from the 2011, 2013 and 2015 cycles of the OSDUHS, a biennial cross-sectional school-based study of children in grades 7 to 12 (N = 9,198). Logistic regression was used to estimate adjusted odds ratios (AOR) in controlled and uncontrolled analyses. Adjusting for sex and grade only, problem gambling was associated with a history of TBI (AOR = 2.8). This association remained significant after adjusting for hazardous drinking and suicidality (AOR = 2.0). In addition, problem gambling had a statistically significant relationship with being male (AOR = 4.7), hazardous drinking (AOR = 4.5), and suicidality (AOR = 3.1). This study provides further data to suggest a link between TBI and problem gambling. However, research is needed on the causal relationship between these variables and the potential implications for treatment and prevention.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".