Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders
Bibliographic record
Abstract
Impulse control is becoming a critical survival skill for the twenty-first century. Impulsivity is implicated in virtually all externalizing behaviours and disorders, and figures prominently in the aetiology and long-term sequelae of substance use disorders (SUDs). Despite its robust clinical and predictive validity, the study of impulsivity is complicated by its multidimensional nature, characterized by a variety of trait-like personality dimensions, as well as by more state-dependent neurocognitive dimensions, with variable convergence across measures. This review provides a hierarchical framework for linking self-report and neurocognitive measures to latent constructs of impulsivity and, in turn, to different psychopathology vulnerabilities, including substance-specific addictions and comorbidities. Impulsivity dimensions are presented as novel behavioural targets for prevention and intervention. Novel treatment approaches addressing domains of impulsivity are reviewed and recommendations for future directions in research and clinical interventions for SUDs are offered. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".