Mentalization Psychotherapy for Patients Presenting with a Psychotic Personality Structure: A Contemporary Approach
Bibliographic record
Abstract
Overt psychotic symptoms, currently understood as resulting from both neurobiological and developmental etiological factors, are also associated with deficits in mentalization in a subgroup of patients presenting with psychotic personality structure. Neurodevelopmental and traumatic impairments seen in this subtype of psychotic disorders create a pressure or need for what has been called a transformational mentalizing process. This specific form of mental elaboration focuses explicitly on finding words and images that help patients to grasp their emotional and mental states. It thus differs from mainstream mentalization treatment, which puts a greater emphasis on reflective functioning. A psychodynamically informed mentalization-based individual and group psychotherapy for this subgroup of patients was developed, aiming at augmenting the patient's psychological resources through explicit transformational mentalization, rather than primarily targeting symptom reduction. This program stimulates curiosity about one's mental states, progressively aiming to give shape to and explore affectively charged mental states, and is integrated with other treatment modalities. This article presents a psychological model of psychotic personality structure and its psychotherapeutic implications, as well as clinical illustrations. Preliminary findings from a pilot study provide encouraging support for the model, including emerging reflective capacities and symptom reduction, as well as overall improvement of social and occupational functioning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".