Examining gambling activity subtypes over time in a large sample of young adults
Bibliographic record
Abstract
Background: Gambling is common in young adulthood. Most young adults phase out of excessive gambling, but some establish regular habits and develop problems. Research has begun to examine the risk associated with different gambling activity patterns. However, there is a paucity of longitudinal work. Using a prospective design, we identified distinct subgroups of young adults based on patterns of gambling activity involvement and tested the stability of these subgroups over 4 years.Method: Data came from the Manitoba Longitudinal Study of Young Adults. Participants (N = 679) completed four waves of self-report measures (spaced 1-year apart). Latent class analysis and latent transition analysis were conducted to identify the number of gambling activity classes and the stability of these over time. Finally, multinomial logistic regressions were conducted to examine correlates of class membership.Results: A three-class model was supported and classes differed based on the degree of gambling involvement (i.e., low, moderate, and high). Only the moderate gambling class was also associated with alcohol dependence, and was the most stable over time. Impulsivity, alcohol use, drug use, and problem gambling symptoms were associated with membership in the moderate (but not the high) gambling class. Participants in the high gambling class were highly likely to transition into either the moderate and low gambling classes four years later.Conclusions: These results demonstrate that high gambling involvement reduces over time in young adulthood. However, our findings suggest that there is a large subgroup of stable moderate gamblers who also tend to engage in other addictive behaviours.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".