Deter the emotions: Alexithymia, impulsivity and their relationship to binge drinking
Bibliographic record
Abstract
INTRODUCTION: The relevance of both emotion processing and impulsivity to alcohol use and misuse is increasingly recognised, yet there is a scarcity of studies addressing their reciprocal interaction. The present study aimed to examine the role that difficulties in emotion processing and trait impulsivity play in explaining binge drinking pattern of alcohol use in student population. We looked at binge drinking, as it is a risk factor to later alcohol abuse and is a common alcohol drinking habit among students. Alexithymia (from Greek as "deter/repel emotions"), a difficulty in identifying and describing feelings in self and others is increasingly recognised as a feature of alcohol misuse. METHODS: One-hundred and seventy-four student alcohol drinkers were assessed for their drinking habits (Alcohol Use Questionnaire), as well as for alexithymia (Toronto Alexithymia Scale) and impulsivity trait (Barratt Impulsiveness Scale); facial emotional expression judgements were also tested. RESULTS: A direct relationships between, both, alexithymia and impulsivity, and binge drinking was found. When combined, trait impulsivity partially mediated the relationship between alexithymia and binge drinking. Facial emotional expression judgements also showed a relationship with binge drinking. CONCLUSIONS: These findings highlight the importance of both emotion processing and impulsivity in understanding binge drinking and indicate potential routes for prevention and intervention techniques, especially towards those who may be at risk of later alcohol abuse.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".