The role of alexithymia, boredom, desire thinking, and subjective risk intelligence in individuals with gambling disorder: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: This study aimed to explore the association between alexithymia, boredom, desire thinking, and subjective risk intelligence in individuals with gambling disorder (GD).METHODS: A total of 94 participants (47 disordered gamblers and 47 control subjects) completed the following questionnaires: Toronto Alexithymia Scale - 20 (TAS-20), Multidimensional State Boredom Scale (MSBS), Desire Thinking Questionnaire (DTQ), Subjective Risk Intelligence Scale (SRIS).RESULTS: The subjects with GD fall within the borderline range of the TAS-20 scores, with higher scores in the dimension of difficulty in identifying feelings (M=20.04, SD=7.25 vs. M=14.09, SD=2.68, P<0.001; d=1.08) and in the externally oriented thinking style (M=25.45, SD=2.95 vs. M=21.15, SD=5.33, P<0.001; d=0.99). Furthermore, the GD group has high scores of boredom and desire thinking than the control group. Finally, GD subjects showed lower SRIS total scores than the control ones, although the difference between the two groups was not statistically significant (M=65.45, SD=9.87 vs. M=68.34, SD=4.51, P=0.07; d=0.37). All variables investigated in the study were significantly correlated in the clinical group whereas no statistically significant correlations were shown in the control group.CONCLUSIONS: This study seems to confirm that alexithymia, boredom, subjective risk intelligence, and desire thinking are strictly related in participants with GD. More specifically, the cognitive processes involved in desire thinking have an important role in the onset and maintenance of GD and need further investigation. Furthermore, this study underlines an interesting relationship between subjective risk intelligence and alexithymia in gamblers, which has not yet been adequately investigated in the literature on the topic. For this reason, our data represent an important starting point for future studies on this point.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".