Bodily confusion: Lower differentiation of emotional and physiological states in student alcohol users
Bibliographic record
Abstract
BACKGROUND: Alexithymia, difficulty in recognising and naming emotions, is common among people who use alcohol. There is also emerging evidence that people with alexithymia are unable to distinguish emotions from non-emotional physiological states. The project aimed to test if alcohol use is related to the way student drinkers experience emotions and physiological states in the body. METHODS: We employed a novel method to study bodily sensations related to emotions and physiological states in the context of alcohol use: the emBODY tool, which allowed participants to mark areas of the body in which they experience various emotions and physiological states. RESULTS: Students who showed a hazardous pattern of alcohol use (alcohol use disorders identification test [AUDIT] score ≥ 7, N = 91), overall, presented higher alexithymia levels and coloured larger areas for emotions and physiological states (showed less specificity) than those who show low-risk alcohol consumption (AUDIT ≤ 4, N = 90). Moreover, statistical classifiers distinguished feeling-specific maps less accurately for hazardous drinkers than low-risk drinkers [F(1,1998) = 441.16; p < 0.001], confirming that higher alcohol use is related to higher confusion of emotional and non-emotional bodily feelings. CONCLUSIONS: Plausibly, this increased bodily confusion drives alcohol consumption: alcohol may serve as a means of dealing with undifferentiated changes in psychophysiological arousal accompanying emotional states.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".