Longitudinal links between impulsivity, gambling problems and depressive symptoms: a transactional model from adolescence to early adulthood
Bibliographic record
Abstract
BACKGROUND: Research shows high co-morbidity between gambling problems and depressive symptoms, but the directionality of this link is unclear. Moreover, the co-occurrence of gambling problems and depressive symptoms could be spurious and explained by common underlying risk factors such as impulsivity and socio-family risk. The goals of the present study were to examine 1) whether common antecedent factors explain the concurrent links between depressive symptoms and gambling problems, and 2) whether possible transactional links between depressive symptoms and gambling problems exist from late adolescence to early adulthood. METHODS: A total of 1004 males from low SES areas participated in the study. RESULTS: Analyses revealed a positive predictive link between impulsivity at age 14 and depressive symptoms and gambling problems at age 17. In turn, gambling problems at age 17 predicted an increase in depressive symptoms from age 17 to age 23, and depressive symptoms at age 17 predicted an increase in gambling problems from age 17 to age 23. CONCLUSIONS: Common antecedent factors may explain the initial emergence of an association between depressive symptoms and gambling problems in adolescence. However, once emerged, their escalation seems to be better explained by a mutual direct influence between the two sets of disorders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".