Modes: Cohesive personality states and their interrelationships as organizing concepts in psychopathology.
Bibliographic record
Abstract
We propose a transdiagnostic approach that centers on modes, state-like manifestations of personality that function as cohesive organizational units. Modes are characterized by specific profiles of affects, behaviors, cognitions, and desires that tend to be coactivated. Each mode is typically experienced as having its own distinct experiential and agentic qualities. A mode-based approach to psychopathology builds on recent analytic and methodological developments which demonstrate the value of modeling personality states dynamically, as well as on longstanding theoretical and empirical traditions that highlight the pragmatic clinical utility of such conceptualizations. We seek to illustrate how the conceptualization of psychopathology in terms of modes and their dynamic interrelations holds considerable transdiagnostic promise. As background, we review both theory and research from philosophical accounts of selfhood, developmental psychology, social and personality psychology, and diverse psychotherapy models that lay the foundation for this mode-based approach to psychopathology. We elaborate on this foundation and (in Section 1 of our online supplemental materials) provide examples of the approach's explicit or implicit relevance to several classes of psychopathology, including dissociative, trauma-related, mood, anxiety, obsessional, substance, psychotic, and personality disorders. After addressing the clinical utility of mode-based conceptualizations, we lay out a research blueprint for assessing and modeling modes, and (in Section 2 of the online supplemental materials) present a broader research agenda highlighting intriguing empirical questions regarding modes in psychopathology. We conclude by noting that the time seems ripe for modes to be (re-)introduced as an organizing construct for understanding psychopathology and personality. (PsycInfo Database Record (c) 2023 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".