Trait Repetitive Negative Thinking: A Brief Transdiagnostic Assessment
Bibliographic record
Abstract
Repetitive negative thinking (RNT) is an established transdiagnostic process associated with multiple emotional disorders. Brief transdiagnostic measures of RNT uncontaminated with diagnosis-specific symptoms, terminology, and instructions are required for (a) research investigating the process of RNT and (b) clinical practice to guide case formulations, treatment plans, and to assess change. The aim of this study was to examine the psychometric properties of a 10-item trait version of the Repetitive Thinking Questionnaire (RTQ-10) in undergraduate (N = 386) and clinical (N = 400) samples. The undergraduate sample completed the RTQ-10, and the clinical sample completed the RTQ-10 as well as measures of worry, rumination, anxiety- and depression-related cognitions, and positive and negative affect. Results demonstrated that the RTQ-10 has a unitary structure, high internal reliability, distinguishes between clinical and non-clinical cases, assesses RNT similarly in men and in women, and accurately assesses RNT along its full continuum. RTQ-10 scores were positively associated with worry and rumination, anxiety and depression symptoms and cognitions, and with the higher order vulnerability factor of negative affect, adding to its transdiagnostic credentials. The RTQ-10 was negatively but weakly associated with positive affect, providing some divergent validity. The RTQ-10 appears to be a brief and clinically useful transdiagnostic measure of RNT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".