Problem gambling symptomatology and alcohol misuse among adolescents: A parallel-process latent growth curve model.
Bibliographic record
Abstract
The objective of the current study was to examine the possible temporal associations between alcohol misuse and problem gambling symptomatology from adolescence through to young adulthood. Parallel-process latent growth curve modeling was used to examine the trajectories of alcohol misuse and symptoms of problem gambling over time. Data were from a sample of adolescents recruited for the Leisure, Lifestyle, and Lifecycle Project in Alberta, Canada (n = 436), which included 4 assessments over 5 years. There was an average decline in problem gambling symptoms followed by an accelerating upward trend as the sample reached the legal age to gamble. There was significant variation in the rate of change in problem gambling symptoms over time; not all respondents followed the same trajectory. There was an average increase in alcohol misuse over time, with significant variability in baseline levels of use and the rate of change over time. The unconditional parallel process model indicated that higher baseline levels of alcohol misuse were associated with higher baseline levels of problem gambling symptoms. In addition, higher baseline levels of alcohol misuse were associated with steeper declines in problem gambling symptoms over time. However, these between-process correlations did not retain significance when covariates were added to the model, indicating that one behavior was not a risk factor for the other. The lack of mutual influence in the problem gambling symptomatology and alcohol misuse processes suggest that there are common risk factors underlying these two behaviors, supporting the notion of a syndrome model of addiction. (PsycINFO Database Record
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".