Negative feedback‐seeking in depression: The moderating roles of rumination and interpersonal life stress
Bibliographic record
Abstract
OBJECTIVES: Swann's self-verification theory proposes that negative feedback seeking (NFS)-the solicitation of negative feedback from others that confirms one's self-views-works in a negative cycle to maintain and exacerbate depression in the face of interpersonal stress. We propose a cognitive-interpersonal integration account of NFS such that this maladaptive behavior prospectively predicts depression only among those with a trait tendency to ruminate on the causes and consequences of depressed mood and stress. METHOD: Participants included 91 young adults who were over-sampled for a lifetime history of a unipolar depressive disorder (age 17-33; 69% women; 67% lifetime depressive disorder). At baseline, participants completed a structured diagnostic interview and self-report measures of NFS, rumination, and depression symptoms. In addition, participants engaged in an interpersonal rejection task (the Yale Interpersonal Stressor) followed by a behavioral measure of NFS. At a 3-month follow-up, depression symptoms were again assessed by self-report and exposure to stressful interpersonal life events in the intervening period were assessed with a rigorous contextual interview and independent rating system. RESULTS: Controlling for baseline depression severity, greater self-reported, and behaviorally assessed NFS predicted greater follow-up depression severity, but only among those with higher trait tendency to ruminate. For self-reported NFS, this association was further moderated by level of interpersonal, but not noninterpersonal, life events experienced over follow-up. CONCLUSION: These findings suggest that rumination may represent a modifiable intervention target that could break the vicious interpersonal cycle of depression and, thus, mitigate the depressogenic effects of NFS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".