Heavy Episodic Drinking is a Trait-State: A Cautionary Note
Bibliographic record
Abstract
BACKGROUND: Heavy episodic (binge) drinking is common in and problematic for undergraduates. Researchers often assume that an individual's heavy episodic drinking is stable and trait-like. However, this fails to consider fluctuating, state-like variation in heavy episodic drinking. This study proposes and tests a novel conceptualization of heavy episodic drinking as a trait-state wherein the contribution of both trait-like stability and state-like fluctuations are quantified. It was hypothesized that heavy episodic drinking is a trait-state such that individuals have trait-like tendencies to engage in heavy episodic drinking, and state-like differences in the expression of this tendency over time. METHODS: A sample of 114 first-year undergraduates from a Canadian university completed self-report measures of heavy episodic drinking at 3 time points across 130 days. Hypotheses were tested with repeated-measures analysis of variance (ANOVA), test-retest correlations, and generalizability theory analyses. RESULTS: A substantial proportion of the variance in heavy episodic drinking is attributable to trait-like stability, with a smaller proportion attributable to state-like fluctuations. CONCLUSIONS: The heavy episodic drinker seems characterized by a stable, trait-like tendency to drink in a risky manner, and this trait-like tendency seems to fluctuate in degree of expression over time. Findings complement research suggesting that people have trait-like predispositions that increase their risk for heavy episodic drinking. However, despite this stable tendency to drink heavily, the frequency of heavy episodic drinking appears to be at least partly sporadic or situation dependent. These findings serve as a caution to alcohol researchers and clinicians who often assume that a single assessment of heavy episodic drinking captures a person's usual drinking behavior.
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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 | 0.072 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".