Unpacking the p-factor. Associations Between Maladaptive Personality Traits and General Psychopathology in Female and Male Adolescents
Bibliographic record
Abstract
Adolescence is a period of rapid physical, psychological, and neural maturation that makes youth vulnerable to emerging psychopathology, highlighting the need for improved identification of psychopathology risk indicators. Recently, a higher-order latent psychopathology factor (p-factor) was identified that explains latent liability for psychopathology beyond internalizing and externalizing difficulties. However, recent proposals suggest reconceptualizing the p-factor model in terms of impairments in personality encompassing difficulties in both self-regulation (borderline features) and self-esteem (narcissistic features), but this remains untested. To address this, this study examined the p-factor structure and the contribution of borderline and narcissistic features using two cross-sectional data collections. In Study 1, 974 cisgender adolescents (63% assigned females at birth; age range: 13-19; Mage = 16.68, SD = 1.40) reported on internalizing and externalizing problems (YSR) to test via structural equation models (SEM) different theoretical models for adolescent psychopathology. In Study 2, 725 cisgender adolescents (64.5% assigned females at birth; age range: 13-19; Mage = 16.22, SD = 1.32) reported internalizing and externalizing problems (YSR), borderline personality features (BPFSC-11), and narcissistic personality traits (PNI), to explore, via SEM, the contribution of borderline and narcissistic traits to the p-factor and accounting for gender differences. Results confirmed the utility of a bi-factor model in adolescence. Furthermore, findings highlighted the contribution of borderline features and narcissistic vulnerability to general psychopathology. The study provides the first evidence supporting a p-factor model reconceptualized in terms of personality impairments encompassing difficulties in self-regulation and self-esteem in adolescents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".