THE NATURE OF DEPRESSIVE RUMINATION AND ITS CONNECTION WITH DEPRESSIVE SYMPTOMS
Bibliographic record
Abstract
Introduction: Researchers have proposed several theories of depressive rumination. To compare among them, we conducted a joint factor analysis. Methods: An online sample (n = 498) completed four rumination questionnaires and the Beck Depression Inventory. We examined associations between emerging factors and depressive symptoms. Results: Most commonly, people ruminated about solving problems in their lives, followed by the causes or consequences of negative situations. They least commonly ruminated about their symptoms and sadness. Thoughts about symptoms and causes or consequences of negative situations uniquely related to depressive symptoms. There was a circular covariance relation between depressive symptoms, thoughts about causes or consequences, and problem-solving, suggesting that symptoms are regulated by a negative feedback loop involving problem-solving. This feedback was not present unless models included thoughts about causes or consequences, suggesting that these thoughts benefit problem-solving. Discussion: Depressive rumination may be a dynamic process involving various thoughts, with different combinations of thoughts having different consequences for depression.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".