What can we learn from the pervasive linkage of impulsivity and addictive behavior?
Bibliographic record
Abstract
Trait impulsivity is consistently linked with pathological aspects of substance use. This study extends this linkage to an undergraduate population, in which scores on the Barratt Impulsivity scale were found to predict frequent binge drinking and poly-substance use. As the authors note, the acute and chronic effects of alcohol and drugs can promote impulsive behavior by impairing cortical executive functions. In this way, a preexisting tendency to impulsive behavior when coupled with exposure to psychoactive drugs could lead to a vicious cycle of frequent heavy use and a corresponding deterioration in executive functions. The authors observe that their cross-sectional data provide a basis to test this hypothesis in a longitudinal design. The data also raise a number of additional questions. First, what do the findings suggest about the role of screening and especially targeted prevention strategies in post-secondary institutions? More generally, what can we infer from the pervasive linkage of impulsivity and addictive behavior: Could trait impulsivity represent a ‘pro-dromal’ state that mimics the deficits of non-impulsive individuals who have already transitioned to pathological levels of substance use? That is, in a functional sense, are impulsive individuals quasi-addicted from the outset?
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".