7th International Conference on Behavioral Addictions (ICBA 2022) June 20–22, 2022, Nottingham, United Kingdom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 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 it