S-7E5 Tackling naturally occurring gambling craving among problem gamblers with interference
Bibliographic record
Abstract
Introduction:Based on international evidence-based research results, the disorder "Gaming Disorder" was logically suggested for the ICD-11 in the chapter "Disorders due to addictive behaviors".Clinical practice shows that patients with Internet-related disorders can be addicted on (online) computer gaming -but also on specific Internet usage behavior such as chatting, social networks, online purchasing behavior or the consumption of pornographic material (Online Sex Addiction).Method: In a multi-centre, randomized controlled clinical trial (Short-term Treatment of Internet and Computer Game Addiction, STICA) the effectiveness of a cognitive behavioral therapy intervention was examined in 143 patients with computer game and Internet addiction.In addition, further analyzes examined how effective this therapy is in individual sub-forms of Internet-related disorders, especially for Online Sex Addiction.Results: The results show that the presented behavioral therapy is comprehensively effective (10-fold increased chance of being symptom-free at the end of the therapy).In a sub-group analysis, it was also shown, which effectiveness values are to be expected for those affected with Online Sex Addiction.Discussion: One can assume that specific group concepts especially for online sex addiction should be developed.The lecture draws learnings from STICA.We designed a specific psychotherapeutic treatment approach addressing Online Sex Addiction.This newly designed approach is abstinence-focused and combines cognitive behavioral therapy (CBT) and mentalization-based therapy (MBT).Recently, we are testing this approach.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.036 |
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".