Moderate to severe gambling problems and traumatic brain injury: A population-based study
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a common injury characterized by a change in brain function after an external blow to the head and is associated with substance abuse, psychological distress, risk-taking, and impulsivity. Convenience and clinical samples have also linked TBI to problem gambling, but have not ruled out confounding variables such as hazardous drinking and psychological distress. This study examines the relationship between TBI and moderate to severe problem gambling in a general population probability sample controlling for hazardous drinking and psychological distress. The data were obtained from a 2015-2016 cross-sectional general population telephone survey of adults ages 18+from Ontario, Canada (N = 3809). Logistic regression was used to estimate the association as adjusted odds ratios (AOR). Moderate to severe problem gambling was independently associated with a history of TBI after adjusting for potential confounders (AOR: 2.80), and had a statistically significant relationship with psychological distress (AOR = 2.74), hazardous drinking (AOR = 2.69), and lower educational levels (AOR = 0.37). This study provides further data to suggest a link between TBI and moderate to severe problem gambling; however, more research is needed to determine if there is a causal relationship or the potential implications for prevention and treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".