Naturalistic interpersonal behavior patterns differentiate depression and anxiety symptoms in the community.
Bibliographic record
Abstract
Symptoms of depression and anxiety are associated with interpersonal problems that, in turn, exacerbate and maintain these symptoms. The purpose of the present study was to identify patterns of interpersonal behavior characteristic of each syndrome, particularly whether intraindividual variability in interpersonal behavior differentiates between anxiety and depression symptoms. After reporting on depression and anxiety symptoms, community participants recorded their behavior following interpersonal interactions over 21 days. Participants' interpersonal behavior at each event was measured using behavior dimensions from the interpersonal circumplex: dominant, submissive, agreeable, and quarrelsome. Mean levels of behavior and intraindividual variability were computed over events and then regressed on depression and anxiety symptoms using structural equation modeling. Elevations in reported depression and anxiety symptoms were both associated with elevated mean-level quarrelsome and submissive behavior. Independent of mean-level behavior and concurrent depression symptoms, elevated anxiety symptoms were associated with elevated variability in agreeable, dominant, and submissive behavior and with elevated variability in type of interpersonal behavior (i.e., spin). Depression symptoms were unrelated to variability in interpersonal behavior. Results demonstrate that variability in behavior distinguishes anxiety from depression symptoms.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".