Symptoms of borderline personality and related pathologies behave as temporal and contemporaneous networks.
Bibliographic record
Abstract
In contrast to latent variable models suggesting a common etiology, network theory proposes that symptoms of psychopathology co-occur because of direct, dynamic associations among them. We examined how symptoms associated with borderline personality disorder, depression, and anxiety mutually reinforce one another over time, forming a network. We further identified symptoms that drove the network by exerting the most influence on other symptoms. Participants were 37 undergraduate students aged 18 to 26. Following baseline assessment, participants were prompted to answer a Qualtrics-based survey of current symptoms of BPD, depression, and anxiety twice daily for 40 days. Multilevel time-series network analyses were conducted with (a) BPD symptoms alone and (b) BPD, depressive and anxiety symptoms. In the network of BPD symptoms, momentary interpersonal difficulties predicted later dissociation, which predicted later affective fluctuation at the within-person level. Dissociation exerted the strongest influence on the overall symptom network. When depressive and anxiety symptoms were included, the networks identified several cross-disorder connections, such as anhedonia and feeling tense, which highlight potential pathways that describe the comorbidity of BPD with anxiety and depressive syndromes. Overall, cognitive symptoms and dissociation were identified as the most influential symptoms across the networks. This study indicates that BPD, depression, and anxiety symptoms may mutually reinforce one another concurrently and over time. Cognitive symptoms exert the highest influence on the cross-disorder networks, such that they influence BPD, depressive, and anxiety symptoms. Our results support the need of targeting cognitions in the treatment of comorbid BPD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".