Testing and treatment‐by‐attitude in psychotherapy for pathological narcissism: A clinical illustration
Bibliographic record
Abstract
Pathological narcissism is a personality constellation comprising distorted self-image, maladaptive self-esteem regulation, and difficulties in intimate relationships. Patients with elevated pathological narcissism may not necessarily meet criteria for narcissistic personality disorder, and may seek treatment for a range of mental health concerns across various clinical settings. An understanding of key principles of control-mastery theory (CMT) can help clinicians understand the specific goals and challenges of the individual patient with pathological narcissism, and can illuminate ways in which the patient may work in psychotherapy. This paper outlines how patients with pathological narcissism may engage in testing of their pathogenic beliefs, and how therapists can respond in ways that facilitate the patient's sense of safety and foster psychological work. The role of the therapist's attitude is highlighted as a means for countering pathogenic beliefs associated with pathological narcissism. Clinical material from a single case of time-limited supportive psychotherapy will be used to illustrate these principles and associated therapeutic processes. Insights from CMT regarding pathogenic beliefs and the patient's plan for addressing them can help to explain how therapy works or does not work for patients with pathological narcissism.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".