A latent class analysis of young adult gamblers from the Manitoba Longitudinal Survey of Young Adults
Bibliographic record
Abstract
Informed by the Pathways Model, the current study utilized latent class analysis (LCA) to empirically derive subtypes of gamblers based on measures of impulsivity, anxiety, depression, drug use and alcohol dependence. The sample in this study (N = 566) was comprised of young adult gamblers (18–22 years of age) who participated in the Manitoba Longitudinal Survey of Young Adults (MLSYA). Multinomial regression was utilized to examine how demographic variables and participant scores on the Problem Gambling Severity Index (PGSI) predicted membership in gambler classes from the LCA. Results of the LCA revealed three classes of gamblers: emotionally vulnerable, non-problem and impulsive. Multinomial regression showed that older age (i.e. 20–22 years of age), lower income (< $20,000 per year), living independently and PGSI scores were associated with increased odds of being classified as an impulsive gambler. Identifying as European, living independently and PGSI scores were associated with increased odds of being grouped in the emotionally vulnerable class of gambler. These results suggest that young adult gamblers are not a homogeneous group but instead are best understood as falling into different subtypes based on shared characteristics outlined in the Pathways Model.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".