The relationship between antisocial personality and drug craving in Chinese male methamphetamine-dependent patients: the mediating role of alexithymia
Bibliographic record
Abstract
Abstract Background In patients with methamphetamine use disorder (MUD), antisocial personality disorder (ASPD) and alexithymia increase the risk of drug craving, but the relationship between the three of them is unclear. Therefore, this study explored the mediating role of alexithymia in the relationship between ASPD and drug craving.Methods We recruited 524 MUD patients at a drug rehabilitation center in Sichuan Province, China, and assessed ASPD with the Mini International Neuropsychiatric Interview (M.I.N.I.), methamphetamine craving with the Desire for Drugs Questionnaire (DDQ), and alexithymia with the Toronto Affective Disorder Scale (TAS-20).Results Compared with MUD patients without ASPD, MUD patients with ASPD had higher DDQ-desire and intention, DDQ-negative reinforcement and DDQ-total scores, as well as TAS-total and their subscale scores (all p < 0.05). Correlation analyses revealed a significant positive correlation between ASPD, alexithymia and drug craving. Mediating effect analysis further indicated that the relationship between ASPD and drug craving was mediated by alexithymia.Conclusions Our study demonstrates for the first time that alexithymia mediates the relationship between ASPD and drug craving, which may provide a new entry point for treating MUD with comorbid ASPD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".