Abus émotionnel et dissociation : Influence de la mentalisation et des traits de personnalité limite
Bibliographic record
Abstract
Contexte. Les relations entre l’abus émotionnel, les traits du trouble de personnalité limite (TPL) et la dissociation sont peu étudiées à l’adolescence. Objectif. L’objectif de cette étude est d’explorer l’association entre ces trois variables chez les adolescents et évaluer si les traits du TPL médient la relation entre l’abus émotionnel et la dissociation. Méthode. Un échantillon de 125 adolescents âgés entre 12 et 21 ans a complété des mesures autorapportées portant sur l’abus émotionnel, les traits du TPL et la dissociation. Résultats. L’abus émotionnel est positivement corrélé avec les traits du TPL et la dissociation. De plus, les traits du TPL médient la relation entre l’abus émotionnel et la dissociation, ce qui signifie que l’effet de l’abus émotionnel sur la dissociation est expliqué par les traits du TPL. Discussion. Ces résultats comblent une lacune dans la littérature concernant le rôle médiateur des traits du TPL dans la relation entre l’abus émotionnel et la dissociation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".