Impulsivité et psychiatrie : de nouveaux outils pour mieux la définir, l’évaluer, la prévenir et la traiter
Bibliographic record
Abstract
Bien que l’impulsivité soit l’une des manifestations les plus couramment rencontrées en psychiatrie et en psychologie clinique, elle demeure difficile à prévoir, à mesurer, à traiter, voire même à définir. Le principal objectif de cette conférence était de proposer une définition claire et opérationnelle du construit multidimensionnel de l’impulsivité, pour ensuite résumer les troubles mentaux qu’elle affecte et présenter des instruments de mesure plus sensibles susceptibles de faciliter l’évaluation psychiatrique au quotidien. Il s’agissait également de proposer l’utilisation en psychiatrie de techniques neurologiques peu coûteuses et faciles d’accès pour non seulement évaluer les risques d’impulsivité individuels, mais aussi pour les diminuer. Ces outils pourraient s’implanter dans tout milieu clinique et s’avérer complémentaires aux approches conventionnelles.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".