A longitudinal examination of gambling subtypes in young adulthood
Bibliographic record
Abstract
In previous research informed by the Pathways Model (an aetiological framework for problem and disordered gambling), latent mixture modelling was used to identify subtypes of gamblers based on measures of impulsivity, anxiety, depression, drug use, and alcohol dependence. The current study extended these findings by: (a) determining if similar subtypes would be identified in the same sample two years later; and (b) utilizing latent transition analysis (LTA) to determine if class membership remained stable over this time period. The sample (N = 566) included young adult gamblers. In line with previous work on Wave 2 of these data and theoretical considerations, a three class model of gamblers was retained at Wave 4: Non-Problem, Emotionally Vulnerable, and Impulsive. The LTA suggested that the majority of Non-Problem gamblers remained in the same class over time. In contrast, Emotionally Vulnerable gamblers were most likely to transition into the Non-Problem Gambler class, and Impulsive Gamblers were equally likely to transition into the Non-Problem and Emotionally Vulnerable classes. Our study provides evidence for the subtypes of gamblers outlined in the Pathways Model. It is also the first study to provide evidence that membership within Emotionally Vulnerable and Impulsive gambling subtypes is unstable during young adulthood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".