Treating Narcissistic Disorders in General Psychiatry: Practical Application of Transference-Focused Psychotherapy Principles
Bibliographic record
Abstract
Patients with primary or co-occurring narcissistic disorders are seen routinely in general psychiatry settings. Contemporary trends in training and practice have impacted psychiatrists' skills and confidence in identifying and treating these disorders, which can range from relatively benign to high-acuity presentations. The goal of this article is to introduce key principles derived from transference-focused psychotherapy (TFP) for use by clinicians in general practice in their work with patients with narcissistic disorders, even when those clinicians do not routinely provide individual psychotherapy. Practical application of TFP principles in work with patients with narcissistic disorders in general psychiatry are proposed, including in diagnostic evaluation, family engagement, prescribing, and safety assessment and risk management calculus. Many psychiatrists whose practices are focused primarily on psychopharmacology, or a "medical model," may not appreciate fully the impact of pathological narcissism in their work. Clinicians who may benefit from familiarity with TFP principles in work with patients with narcissistic disorders include the approximately one-half of U.S. psychiatrists who do not offer psychotherapy in their practice.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".