Drinking motives and attentional bias to affective stimuli in problem and non-problem drinkers.
Bibliographic record
Abstract
Problem drinking may reflect a maladaptive means of coping with negative emotions or enhancing positive emotions. Disorders with affective symptoms are often characterized by attentional biases for symptom-congruent emotionally valenced stimuli. Regarding addictions, coping motivated (CM) problem gamblers exhibit an attentional bias for negative stimuli, whereas enhancement motivated (EM) problem gamblers exhibit this bias for positive stimuli (Hudson, Jacques, & Stewart, 2013). We predicted that problem drinkers would show similar motive-congruent attentional biases. Problem and non-problem drinkers (n = 48 per group) completed an emotional orienting task measuring attentional biases to positive, negative, and neutral stimuli. As predicted, EM problem drinkers showed an attentional bias for positive information (i.e., reduced accuracy for positively cued trials). However, CM problem drinkers displayed a general distractibility (i.e., reduced accuracy, regardless of cue valence). The results add further support for Cooper et al.'s (1992) motivational model of alcohol use, and indicate potential motivation-matched intervention targets. (PsycINFO Database Record
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".