Bibliographic record
Abstract
AbbreviationsCB compulsive buyingCBT cognitive-behavioural therapyCSB compulsive sexual behaviourDSM Diagnostic and Statistical Manual of Mental DisordersPG pathological gamblingThe concept of behavioural addiction as a way to classify behaviours that mirror the symptoms and consequences of classic alcohol and drug addictions has become of great interest to researchers and clinicians.' This is, in part, due to ongoing discussions regarding the creation of a category for behavioural addictions within the general class of substance use disorders in DSM-5, scheduled for release in May 2013.2 To date, PG is the only proposed member of the category, but it signals the recognition by the scientific community of what the National Institute on Drug Abuse considers relatively pure models of addiction because the presence of an exogenous substance does not contaminate their processes.3What defines a behavioural addiction? The simplest definition is that these are disorders whose overt symptoms are behaviourally expressed, and are viewed-at least initially-as pleasurable (for example, gambling, sex, shopping, and Internet use), and have attained an irresistible quality, such as the substance addictions. The boundaries of this emerging category are relatively fluid, appearing to expand or contract depending on an expert's own particular views. Some investigators choose to include impulse control disorders, currently listed in DSM-IV-TR (for example, PG, kleptomania, and pyromania) as members of the behavioural addiction category, but have also included disorders not currently recognized in the DSM system (for example, CB, Internet addiction, and CSB).3·4 In this edition of The Canadian Journal of Psychiatry, both Dr Jon ? Grant and colleagues5 and Dr Robert F Leeman and Dr Marc ? Potenza6 write about the behavioural addictions, yet appear to disagree about its members. For example, Dr Grant and colleagues5 include pyromania and binge eating disorder, but Dr Leeman and Dr Potenza6 do not. Conversely, Dr Leeman and Dr Potenza6 include video game playing, but Dr Grant and colleagues5 do not. This perfectly illustrates how, even among those actively writing on the topic, there remains disagreement of its breadth. And while classification should rest on research evidence, the data we have are imperfect and subject to varying interpretation.While controversy remains about the optimal categorization of the behavioural addictions, research evidence supports the linkage between these disorders and substance addictions, strengthening the rationale to include both behavioural and substance addictions in the same general class. One of the benefits from recognizing this category is that improved classification could enable a more accurate description of endophenotype and biological markers that characterize these conditions. More precise classification could lead to specific treatments.Scientists and others writing about behavioural addictions have described common elements that link them with substance addiction. A growing body of phenomenological, genetic, and neurobiological evidence supports a relation among proposed behavioural addictions to the substance addictions.4·7 As outlined in this issue by Dr Grant and colleagues5 and Dr Leeman and Dr Potenza,6 they share common core clinical features. For example, both involve the performance of repetitive or compulsive behaviours, despite negative consequences; diminished control over the behaviours; craving prior to engaging in the behaviour, and experiencing a pleasurable response while engaged in the behaviour.4 Further, they appear to share features of tolerance, withdrawal, repeated attempts to cut back, and impairment in multiple life domains.4·8·9 Phenomenological data also suggest a relation between the behavioural and substance addictions. They often begin in the late teens or early twenties, and while several of the behavioural addictions, such as CB and kleptomania, are more common in women,10· other behaviourally expressed addictions appear to have a male preponderance (for example, PG and CSB), similar to that seen in substance addictions. …
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".