Stability of negative self‐structures: A longitudinal comparison of depressed, remitted, and nonpsychiatric controls
Bibliographic record
Abstract
To be considered a vulnerability marker for depression, a variable should, in addition to demonstrating sensitivity and specificity, also show evidence of temporal stability (i.e., remain present in the absence of depressive symptomatology). Although many cognitive factors are associated with depression, the majority of them appear to be episode rather than vulnerability markers. This study examined cognitive organization of positive and negative interpersonal and achievement content in clinically depressed, remitted, and nonpsychiatric controls. At initial assessment, a sample of 54 clinically depressed individuals and 37 never-depressed controls completed self-report measures of positive and negative automatic thoughts and two cognitive organizational tasks. They were retested 6 months later when half of the depressed group no longer met diagnostic criteria for major depression. Negative automatic thoughts decreased and positive automatic thoughts increased significantly in individuals who had improved clinically. The organization of negative interpersonal content remained stable despite symptom amelioration, but negative achievement content was less interconnected at follow-up in those patients who had improved. The structure of relational schemas, in particular, appears to be stable and may be an important cognitive vulnerability factor for depression.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".