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 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 | 0.001 |
| 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 | 0.001 |
| 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".