Compulsive features in behavioural addictions: the case of pathological gambling
Bibliographic record
Abstract
AIMS: To describe, in the context of DSM-V, how a focus on addiction and compulsion is emerging in the consideration of pathological gambling (PG). METHODS: A systematic literature review of evidence for the proposed re-classification of PG as an addiction. RESULTS: Findings include: (i) phenomenological models of addiction highlighting a motivational shift from impulsivity to compulsivity associated with a protracted withdrawal syndrome and blurring of the ego-syntonic/ego-dystonic dichotomy; (ii) common neurotransmitter (dopamine, serotonin) contributions to PG and substance use disorders (SUDs); (iii) neuroimaging support for shared neurocircuitries between 'behavioural' and substance addictions and differences between obsessive-compulsive disorder (OCD), impulse control disorders (ICDs) and SUDs; (iv) genetic findings more closely related to endophenotypic constructs such as compulsivity and impulsivity than to psychiatric disorders; (v) psychological measures such as harm avoidance identifying a closer association between SUDs and PG than with OCD; (vi) community and pharmacotherapeutic trials data supporting a closer association between SUDs and PG than with OCD. Adapted behavioural therapies, such as exposure therapy, appear applicable to OCD, PG or SUDs, suggesting some commonalities across disorders. CONCLUSIONS: PG shares more similarities with SUDs than with OCD. Similar to the investigation of impulsivity, studies of compulsivity hold promising insights concerning the course, differential diagnosis and treatment of PG, SUDs, and OCD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".