Co-occurring substance-related and behavioral addiction problems: A person-centered, lay epidemiology approach
Bibliographic record
Abstract
Background and aims The aims of this study were (a) to describe the prevalence of single versus multiple addiction problems in a large representative sample and (b) to identify distinct subgroups of people experiencing substance-related and behavioral addiction problems. Methods A random sample of 6,000 respondents from Alberta, Canada, completed survey items assessing self-attributed problems experienced in the past year with four substances (alcohol, tobacco, marijuana, and cocaine) and six behaviors (gambling, eating, shopping, sex, video gaming, and work). Hierarchical cluster analyses were used to classify patterns of co-occurring addiction problems on an analytic subsample of 2,728 respondents (1,696 women and 1032 men; Mage = 45.1 years, SDage = 13.5 years) who reported problems with one or more of the addictive behaviors in the previous year. Results In the total sample, 49.2% of the respondents reported zero, 29.8% reported one, 13.1% reported two, and 7.9% reported three or more addiction problems in the previous year. Cluster-analytic results suggested a 7-group solution. Members of most clusters were characterized by multiple addiction problems; the average number of past year addictive behaviors in cluster members ranged between 1 (Cluster II: excessive eating only) and 2.5 (Cluster VII: excessive video game playing with the frequent co-occurrence of smoking, excessive eating and work). Discussion and conclusions Our findings replicate previous results indicating that about half of the adult population struggles with at least one excessive behavior in a given year; however, our analyses revealed a higher number of co-occurring addiction clusters than typically found in previous studies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".