Subtypes of narcissistic personality disorder based on psychotherapy process: A longitudinal nonparametric analysis.
Bibliographic record
Abstract
The present study aims at empirically exploring subtypes of narcissistic personality disorder (NPD), based on patient descriptors of the psychotherapeutic process. Subtype identification and characterization of NPD is central, in particular, to increase diagnostic precision, linking categorical and dimensional conceptualizations of psychopathology, and to individualize treatments. A total of N = 161 patients diagnosed with NPD undergoing clarification-oriented psychotherapy were included in the present reanalysis of a naturalistic pre-post process-outcome study. At three crucial time-points of the therapy (Sessions 15, 20, and 25), the patient's in-session quality of content, process, and relationship are assessed using intensive video- and audio analyses. Levels of psychopathology were assessed using self-reported questionnaires. Data were analyzed using longitudinal nonparametric analysis. Based on in-session processes across three time-points, a two-subtype solution was retained (optimal vs. suboptimal process qualities). Optimal process quality of time was linked with the intensity of narcissistic symptoms; suboptimal process quality was linked with a variety of general symptom loads and problematic personality traits. The two empirical subtypes were predicted by the quality of real-life functioning with an accuracy of more than 92% and were partially associated with outcome. NPD may be empirically differentiated between patients engaging in optimal psychotherapy process versus those who engage in suboptimal psychotherapy process. This differentiation has reliable clinical predictors at the outset of treatment. The present study has implications in terms of personalizing psychotherapy for patients presenting NPD, or pathological narcissism. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".