Exploring the substance and behavioural addiction nexus among people who gamble
Bibliographic record
Abstract
Adverse consequences of problem gambling, addictive substance use, and addictive behaviours are palpable.High co-occurrence rates amongst these behaviours suggest a common etiology.Yet, most research examines addictions in isolation.The current study is a secondary data analysis of the Alberta Gambling Research Institute's National Project to examine the cooccurrence of addictive behaviours in people who gamble.Data from 10,199 Canadian adults were entered into a mixture model to identify subgroups defined by engagement in addictive behaviours.The Pathways Model of Disordered Gambling was used as a framework.We hypothesized that at least four classes would emerge-one without problems and three or more that aligned with the model.A seven-class model emerged.The classes were generally consistent with the model regarding emotional vulnerability and impulsivity, but demonstrated unique patterns of substance use and addictive behaviours.These findings underscore the need to examine patterns of co-occurrence for addictions.Above all, I want to express my heartfelt appreciation to my supervisor, Dr. Nassim Tabri.His invaluable guidance, expertise, and unwavering encouragement have been pivotal throughout this journey.Next, I extend my gratitude to the esteemed members of my Master's Defense committee: Dr. Michael
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".