Unique versus shared associations between self-reported behavioral addictions and substance use disorders and mental health problems: A commonality analysis in a large sample of young Swiss men
Bibliographic record
Abstract
Background and aims Behavioral addictions (BAs) and substance use disorders (SUDs) tend to co-occur; both are associated with mental health problems (MHPs). This study aimed to estimate the proportion of variance in the severity of MHPs explained by BAs and SUDs, individually and shared between addictions. Methods A sample of 5,516 young Swiss men (mean = 25.47 years old; SD = 1.26) completed a self-reporting questionnaire assessing alcohol, cannabis, and tobacco use disorders, illicit drug use other than cannabis, six BAs (Internet, gaming, smartphone, Internet sex, gambling, and work) and four MHPs (major depression, attention-deficit hyperactivity disorder, social anxiety disorder, and borderline personality disorder). Commonality analysis was used to decompose the variance in the severity of MHPs explained ( R 2 ) by BAs and SUDs into independent commonality coefficients. These were calculated for unique BA and SUD contributions and for all types of shared contributions. Results BAs and SUDs explained between a fifth and a quarter of the variance in severity of MHPs, but individual addictions explained only about half of this explained variance uniquely; the other half was shared between addictions. A greater proportion of variance was explained uniquely or shared within BAs compared to SUDs, especially for social anxiety disorder. Conclusions The interactions of a broad range of addictions should be considered when investigating their associations with MHPs. BAs explain a larger part of the variance in MHPs than do SUDs and therefore play an important role in their interaction with MHPs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".