A Multifactorial Conceptualization of Impulsivity
Bibliographic record
Abstract
Abstract. Despite the multiple phenotypic presentations of impulsivity, the underlying factor structure of the construct has yet to be settled. The aim of this study, with two multimethod, multisource datasets, was to further explore the multifactorial nature of impulsivity and propose a measure-selection approach. Unlike previous studies that relied on a single type of statistical analysis, the current study explored the relations between personality and behavioral measures of impulsivity utilizing exploratory factor analysis (EFA) and principal component analysis (PCA). Participants comprised two samples of young adults (n(study 1) = 175 and n(study 2) = 118) from separate communities in southwestern Ontario, Canada. Various facets of impulsivity were assessed including adult ADHD symptoms, planning and organizational skills, executive dysfunction, impulsive personality traits (i.e., sensation-seeking), risk-taking behavior, disinhibition, cognitive flexibility, and delay discounting. Both statistical analyses yielded two-factor models. The Dysexecutive Control factor reflected a tendency to act without thinking or planning, and difficulty focusing for a sustained period of time. The Reward-Seeking factor reflected a general need for excitement, and a preference for novel situations despite adverse consequences. For the purposes of standardized assessment of cognitive, emotional, and behavioral manifestations of impulsivity, trans-theoretical measure selection for research and clinical purposes is discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".