Alexithymia, reward sensitivity and risky drinking: the role of internal drinking motives
Bibliographic record
Abstract
Two personality dimensions, alexithymia and reward sensitivity, are known risk factors for problematic alcohol consumption. Internal or mood-change motives of drinking to cope with negative mood, as well as drinking to enhance positive mood (“get high”), have also been implicated as risk factors. The present study sought to determine whether the association between alexithymia and risky drinking is mediated by the motive of drinking to cope with negative mood, and whether the association between reward sensitivity and risky drinking is mediated by the motive of drinking to enhance positive mood. Social drinkers aged 18–45 years were recruited from an Australian university and the local community, with the final sample consisting of 155 participants (80 females, 75 males). They completed an online questionnaire battery that included the Toronto Alexithymia Scale (TAS-20), Depression Anxiety Stress Scales 21 (DASS-21), Drinking Motives Questionnaire – Revised (DMQ-R), Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), and Alcohol Use Disorders Identification Test (AUDIT). The positive relationship between TAS-20 alexithymia and AUDIT index of risky drinking was mediated by coping motives for drinking, with the relationship of TAS-20 to the latter mediated by negative mood as indexed by DASS-21. Further, the positive relationship between SPSRQ sensitivity to reward scores and AUDIT was mediated by enhancement motives for drinking. Although results were obtained in a non-clinical sample, they are consistent with the differential drinking motives said to characterize Type I versus Type II alcoholism and suggest distinct trajectories from inherent personality traits to problematic drinking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".