Structure factorielle de la traduction française de l'échelle d'impulsivité de Barratt (BIS-10)
Bibliographic record
Abstract
Though the concept of impulsiveness is controversial, there are many attempts being made to measure this dimension. In this context, only psychometric measures are widely considered valid and are routinely in use. Barratt developed the first scale that specifically measured impulsiveness. Subsequently, various refinements have improved the validity of results. We have translated, without any significant problems, the tenth validated version of this scale (BIS 10) into French, and we have completed a factorial analysis. The scale was coupled with a self-administered questionnaire designed to assess anxiety. A sample of 280 subjects between the ages of 18 and 79 years (average age, 36.9) were recruited from the general population. Subject age was found to have a weak but nevertheless significant correlation with the impulsiveness rating. A principal component analysis (PCA) resulted in the first 9 factors explaining 55.6% of the variance. Another PCA of these factors allowed the identification of a second tier of 3 second order factors; these were closely related to Barratt's ranking. Our study confirms results from the scale's initial analysis--results which could not be subsequently reproduced. To our knowledge, this is the first French translation of an instrument that specifically measures impulsiveness and the first in which a factorial structure has been tested in the general population.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".