Attachment and Gambling Severity Behaviors Among Regular Gamblers: A Path Modeling Analysis Exploring the Role of Alexithymia, Dissociation, and Impulsivity
Bibliographic record
Abstract
Abstract Gambling disorder is viewed by many as a behavioral addiction involving significant functional impairment and a deterioration in the quality of life. The aim of the present study was to explore the factors that can influence problematic gambling by specifically focusing on the role of attachment, alexithymia, dissociation, and impulsivity. The sample comprised 368 regular gamblers (59% males, 41% females; Mage=33.5 years). They completed an online survey consisting of the South Oaks Gambling Screen, Relationship Questionnaire, Twenty-Items Toronto Alexithymia Scale, Dissociative Experiences Scale‐II, and Barratt Impulsiveness Scale–11. Path modeling was performed to analyze the collected data. Results showed a significant multiple mediation model: CMIN/DF = 4.447, GFI = 0.984, NFI = 0.964, CFI = 0.971, SRMR = 0.046. Fearful and preoccupied attachment patterns showed significant and positive associations with problematic gambling, and which were mediated by alexithymia, dissociation, and impulsivity. These results provide useful information to orient clinical practice and preventive intervention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".