Omnibus Rumination Inventories Consistently Reveal Unconstructive, Constructive, and Positive Repetitive Thought as Rumination's Major Factors
Bibliographic record
Abstract
Numerous questionnaires and scales have been developed to measure trait rumination and related constructs (e.g., perseverative cognition, self-preoccupation, repetitive thought, etc.) over the past three decades. These tools measure various depressive, dysphoric, anxious, angry, positive, stress-related, trauma-related, illness-related, and goal-related forms of repetitive thought. Evaluations of the distinctiveness of these constructs has been limited to piecemeal comparisons of scale/subscale total scores during each scale’s development and psychometric evaluation, affording no higher-order view of the key constructs in this theoretical domain. Here, we took the novel approach of synthesizing items from 87 prevailing and representative rumination and repetitive thought subscales into two versions of an elaborate inventory for statistical exploration. Exploratory factor analysis of 821 online responses that was cross-validated between the two versions of the inventory revealed a reliable three-factor structure not fully captured by any existing instrument: Unconstructive Rumination, Constructive Rumination, and Positive Rumination. Lower-order models were also supported, in which several Unconstructive Rumination subfactors were observed: Negative Rumination, Angry Rumination, Illness Rumination, Pessimistic/Hopeless Rumination, and Counterfactual/Self-Critical Rumination. This structure is much more parsimonious than the abundance of existing scales would suggest. They also support the notion that rumination does not simply involve negative cognitions, as traditionally thought, but constructive and positive ones as well. Researchers and clinicians are encouraged to distinguish among these different kinds of rumination in empirical investigations and clinical practice.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".