Decoupling Personality and Acute Psychiatric Symptoms in a Depressed Sample and a Community Sample
Bibliographic record
Abstract
The association between depression and neuroticism is complex; however, because of the difficulty in assessing neuroticism during mood episodes, the mechanisms underlying this relationship remain poorly understood. In this study, we sought to decompose neuroticism into finer grained elements that were uncorrelated with psychiatric symptoms and examine the incremental validity of those elements in explaining deficits in interpersonal functioning. A bifactor model with one general factor and six specific factors fit the data well in both a depressed ( N = 807) and a community ( N = 1,284) sample, and the specific factors were relatively independent of acute symptoms. Moreover, two specific factors (Angry Hostility and Self-Consciousness) accounted for incremental variance in interpersonal functioning problems in the community sample and a subgroup of depressed participants. The results demonstrate that neuroticism can be decomposed into components that are distinct from symptoms and incrementally associated with deficits in interpersonal functioning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".