Conceptual overlap of negative thought processes in insomnia: A focus on catastrophizing, worry, and rumination in a student sample
Bibliographic record
Abstract
OBJECTIVES: The association and overlap between different forms of negative thought processes in insomnia is largely unknown. The purpose of the current investigation was to examine conceptual overlap between three insomnia-specific negative thought processes; catastrophizing, worry, and rumination, identify the underlying factors, and explore their associations with insomnia symptoms. METHODS: A total of 360 students completed three insomnia-related negative thought process scales (Catastrophic Thoughts about Insomnia Scale, Anxiety and Preoccupation about Sleep Questionnaire, Daytime Insomnia Symptom Response Scale) and two insomnia symptoms measures (the Insomnia Severity Index and Sleep Condition Indicator). RESULTS: The three scales and their subscales displayed acceptable reliabilities. Further, confirmatory factor analysis was supportive of the notion of catastrophizing, worry, and rumination measures as distinct. The catastrophizing and worry constructs were significantly associated with insomnia symptoms, but the rumination factor was not. CONCLUSIONS: The findings indicate that catastrophizing, worry, and rumination might be viewed as distinct constructs. Although more research is warranted on the topic of conceptual overlap, the current results might have implications for the development of models of insomnia, clinical research, and 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".