Predicting future harm from gambling over a five-year period in a general population sample: a survival analysis
Bibliographic record
Abstract
BACKGROUND: There is little longitudinal evidence on the cumulative risk of harm from gambling associated with excess spending and frequency of play. The present study sought to assess the risk of gambling problems over a five-year period in adults who exceed previously derived low-risk gambling limits compared to those who remain within the limits after controlling for other modifiable risk factors. METHODS: Participants were adults (N = 4212) drawn from two independent Canadian longitudinal cohort studies who reported gambling in the past year and were free of problem gambling at time 1. Multivariate Cox regression was employed to assess the impact over time of gambling above low-risk gambling thresholds (frequency ≥ 8 times per month; expenditure ≥75CAD per month; percent of household income spent on gambling ≥1.7%) on developing moderate harm and problem gambling. Covariates included presence of a DSM5 addiction or mental health disorder at time 1, irrational gambling beliefs, number of stressful life events in past 12 months, number of game types played each year, and playing electronic gaming machines or casino games. RESULTS: In both samples, exceeding the low-risk gambling limits at time 1 significantly increased the risk of moderate harm (defined as ≥2 consequences on the Problem Gambling Severity Index [PGSI]) within 5 years after controlling for other modifiable risk factors. Other significant predictors of harm were presence of a mental disorder at time 1, cognitive distortions about gambling, stressful life events, and playing electronic gaming machines or casino games. In one sample, the five-year cumulative survival rate for moderate harm among individuals who stayed below all the low-risk limits was 95% compared to 83% among gamblers who exceeded all limits. Each additional low-risk limit exceeded increased the cumulative probability of harm by 30%. Similar results were found in models when the outcome was problem gambling. CONCLUSIONS: Level of gambling involvement represents a highly modifiable risk factor for later harm. Staying below empirically derived safe gambling thresholds reduces the risk of harm over time.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".