Mentaliser les conflits en thérapie de groupe : détruire et survivre, au service de la cohésion
Bibliographic record
Abstract
The high prevalence of personality disorders, along with their substantial functional impact, are important societal issues, which must be addressed by mental health services. Many treatments have shown significant benefits and have contributed to alleviate the difficulties tied to these disorders. Mentalization-based therapy (MBT), which is constituted of a group therapy modality, is an evidence-based treatment of borderline personality disorder. The mentalization-based group therapy (MBT-G) modality raises many challenges for the psychotherapists. The effectiveness of the group intervention lies, according to the authors, in the capacity to support the mentalizing stance, to stimulate group cohesion, and allows the experience of a healthy and healing process of reappropriation of conflictual situations, situations which, in their opinion, are underutilized in this type of therapeutic process. This article focuses on the interventions that foster a mentalizing attitude. Specifically, we discuss how to focus on the "here and now," how to identify and resolve conflictual situations and how to enhance metacognitions and, hence, group cohesion, while aiming to bonify the therapeutic process.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".