Profiles of impulsivity and alcohol use: Unveiling personality, cognitive traits, and <scp>DSM</scp> diagnoses
Bibliographic record
Abstract
BACKGROUND: Impulsivity is closely associated with alcohol use, but limited research has explored distinct latent profiles encompassing impulsivity traits and alcohol use disorder symptoms. METHODS: This study used latent profile analysis (LPA) to investigate these patterns among 201 adult outpatients (50% female, 50% male) from a tertiary care setting. Participants completed self-reported measures such as the Alcohol Use Disorders Identification Test (AUDIT), Impaired Control Scale, and UPPS-P Impulsivity Scale, as well as performance-based tasks like the Probability Reward Task (PRT) and Stop Signal Reaction Time Task. RESULTS: LPA identified three profiles using AUDIT, impaired control, and UPPS-P: (1) Low-Risk Profile-characterized by low levels of alcohol use disorder (AUD) symptoms and impulsivity; (2) Emotionally Reactive Profile-characterized by elevated impulsivity with low AUD symptoms; and (3) High-Risk Profile-characterized by elevated levels of both AUD symptoms and impulsivity. ANCOVA results revealed that Emotionally Reactive individuals scored higher on neuroticism, negative affectivity, and psychoticism and lower on conscientiousness compared to the Low-Risk group. Both Emotionally Reactive and High-Risk groups showed lower agreeableness, antagonism, and disinhibition relative to the Low-Risk group. On cognitive tasks, the Low-Risk group outperformed the High-Risk group in PRT accuracy and discriminability, while Emotionally Reactive and Low-Risk groups showed similar advantages over High Risk. CONCLUSIONS: These findings reveal distinct personality and cognitive profiles linked to reward and control processes, informing tailored interventions for impulsivity and alcohol-related harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".