The Relationship of Rumination, Worry and OCD Symptoms During Technology Supported Mindfulness Therapy for OCD
Bibliographic record
Abstract
ABSTRACT Background In this study, we re‐examined data from a previous randomized controlled trial investigating ‘technology supported mindfulness’ (TSM)—an 8‐week treatment intervention for individuals experiencing OCD. The current analysis involves an examination of the longitudinal relationships between rumination, worry and OCD symptom changes during mindfulness treatment, in comparison to a waitlist control. Methods Participants experiencing OCD (n = 71) were randomly assigned to 8 weeks of (1) TSM or (2) waitlist control. We tested the extent to which rumination (using the Ruminative Response Scale) and worry (using the Penn State Worry Questionnaire) are associated with OCD symptom changes during the acute phase of treatment, concurrently (i.e., within the same longitudinal model). Results Generalized linear model (GLM) results indicated a significant time (week 1 vs. week 8) by condition interaction involving decreased rumination in the TSM condition: F(1, 61) = 13.37, p = 0.001, partial η2 = 0.18 and observed power = 0.94. A second GLM demonstrated decreased worry in the TSM condition: F(1, 69) = 37.34, p = 0.001, partial η2 = 0.35 and observed power = 0.83. Longitudinal ‘latent difference’ structural equation analyses demonstrated a cross‐lagged association between worry (but not rumination) and OCD symptom changes. Conclusions Individuals in the TSM condition experienced greater reductions in rumination and worry during 8 weeks of TSM treatment compared to the waitlist control, and reduced worry predicted subsequent OCD symptom reduction.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".