Variety of gambling activities from adolescence to age 30 and association with gambling problems: a 15‐year longitudinal study of a general population sample
Bibliographic record
Abstract
AIMS: To estimate trajectories of gambling variety from mid-adolescence to age 30 years, and compare the different trajectory groups with regard to the type and the frequency of gambling activities practiced and gambling-related problems. DESIGN: Prospective longitudinal cohort study. SETTING: Province of Quebec, Canada. PARTICIPANTS: A mixed-gender general population cohort assessed at ages 15 (n=1882), 22 (n=1785) and 30 (n=1358). MEASUREMENTS: Adolescent and adult versions of the South Oaks Gambling Screen (SOGS). FINDINGS: Group-based trajectory analysis identified three distinct trajectories: a late-onset low trajectory (26.7% of sample) initiating gambling at age 22, an early-onset low trajectory (64.8% of sample), characterized by one to two different activities from age 15 onwards and a high trajectory (8.4% of sample), with an average of four to five different activities from age 15 to 30. Males (14.2%) were four times more likely to be on a high trajectory than females (3.5%) (P<0.001). Preferred types of gambling activities were similar across the three trajectories. Participants on a high trajectory reported higher gambling frequency at ages 15 and 30, and were more likely to experience problem gambling at age 30: 3.09 [95% confidence interval (CI)=1.66, 5.75] and 2.26 (95% CI=1.27, 4.04) times more, respectively, than late-onset low and early-onset low participants, even when socio-economic status (SES), frequency of gambling and problem gambling in adolescence, gender, age 30 education, SES and frequency of gambling were controlled. CONCLUSIONS: Engaging in several different types of gambling in early adulthood appears to be a risk factor for emergence of problem gambling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".