Examining Autobiographical Memory Content in Patients with Depression and Anxiety Disorders
Bibliographic record
Abstract
The purpose of this study was to move beyond the traditional specificity model of autobiographical memory (ABM) and to examine the content of memories with a focus on disorder and schema-relevant content. The sample (N = 82) included 25 patients with major depressive disorder (MDD), 24 with social phobia (SP), and 33 with panic disorder with agoraphobia (PDA) who were referred to a large outpatient clinic for group treatment of depression or anxiety. Participants completed the Autobiographical Memory Test (AMT) and Beck Depression Inventory-II as part of the clinical intake process. Responses to the AMT were coded for disorder-specific content based on diagnostic criteria for each disorder as well as for schema-relevant (sociotropy vs. autonomy) content. A repeated measures multiple analysis of variance demonstrated significant differences in disorder-specific content, with patients in the MDD group reporting more depressotypic ABMs than those in the PDA group but not the SP group. Similarly, in the analysis of schema-relevant content, significant differences were found between MDD and PDA regarding the presence of autonomy-based ABM ratings. Study results provide partial support for the cognitive specificity hypothesis with ABM content. The results are discussed in relation to the cognitive models of depression and anxiety.
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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 | 0.000 | 0.005 |
| 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 | 0.001 |
| 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".