Relationships between Behavioural Addictions and Psychiatric Disorders: What Is Known and What Is Yet to Be Learned?
Bibliographic record
Abstract
This article provides a narrative review of the relationships between several behavioural addictions [pathological gambling, problematic Internet use (PIU), problematic online gaming, compulsive sexual behaviour disorder, compulsive buying, and exercise addiction] and psychiatric disorders. Associations between most behavioural addictions and depressive and anxiety disorders are strong and seem relatively non-specific. Strong links with substance use disorders may support the notion that some people are more prone to addictive behaviours, regardless of whether these involve substances or problematic activities. Other associations seem relatively specific, for example, those between PIU/online gaming and attention-deficit/hyperactivity disorder, between compulsive buying on the one hand and eating disorders and hoarding on the other hand and between exercise addiction and eating disorders. The quality of the research varies, but most studies suffer from methodological limitations, including a cross-sectional or correlational design, non-representative study populations, small sample sizes, reliance on self-report assessment instruments, diverse diagnostic criteria, and conceptual heterogeneity of most behavioural addictions. Due to these limitations, generalisability of the findings is questionable and the direction of causality, if any, is unknown in the relationships between behavioural addictions and psychiatric disorders. Regardless of the aetiological uncertainty, these relationships often call for a modified treatment approach. Prospective studies are needed to clarify the longitudinal relationships between behavioural addictions and psychiatric disorders.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".