Alexithymia and gambling: Psychotherapy to differentiate feelings
Bibliographic record
Abstract
Introduction The relationship of alexithymia with gambling addiction is not obvious, but it is present, as evidenced by the results of many studies. Alexithymia is likely to associate with gambling as a coping behavior to increase emotional arousal and avoid negative emotions, according to the affect dysregulation model. Alexithymic individuals experience the same spectrum of emotions as ordinary people, however, from the standpoint of psychology, psychiatry, unexpressed emotions are repressed into the subconscious, and their bodily manifestations accumulate. Objectives We plan to conduct research to improve the medical and psychological support of patients with pathological gambling due to the presence of alexithymia. Methods A systematic search of the literature was run in the major reference databases including PubMed, Cochrane Database for Systematic Review, Web of Science, Scopus until 2019. All studies assessed alexithymia with the Toronto Alexithymia Scale while gambling problems were assessed mostly with the South Oaks Gambling Screen. Results We assume that for pathological gamblers, specific psychotherapeutic techniques like body-centered psychotherapy could help them to differentiate feelings from bodily sensations. Conclusions The results highlight the importance of taking in the relationship between alexithymia and pathological gambling. Further studies are needed to widen the knowledge of this association.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".