Trajectories of gambling problems from mid-adolescence to age 30 in a general population cohort.
Bibliographic record
Abstract
Studies of gambling starting before adulthood in the general population are either cross-sectional, based on the stability of these behaviors between 2 time points, or cover a short developmental period. The present study aimed at investigating the developmental trajectories of gambling problems across 3 key periods of development, mid-adolescence, early adulthood, and age 30, in a mixed-gender cohort from the general population. Using a semiparametric mixture model, trajectories were computed based on self-reports collected at ages 15 (N = 1,882), 22 (N = 1,785), and 30 (N = 1,358). Two distinct trajectories were identified: 1 trajectory including males and females who were unlikely to have experienced gambling problems across the 15-year period, and 1 trajectory including participants likely to have experienced at least 1 problem over the last 12 months at each time of assessment. Participants following a high trajectory were predominantly male, participated frequently in 3 to 4 different gambling activities, and were more likely to report substance use and problems related to their alcohol and drug consumption at age 30. Thus, gambling problems in the general population are already observable at age 15 in a small group of individuals, who maintain some level of these problems through early adulthood, before moderately but significantly desisting by age 30, while also experiencing other addictive behaviors and related problems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".