The Sequential Relation Between Changes in Catastrophizing and Changes in Posttraumatic Stress Disorder Symptom Severity
Bibliographic record
Abstract
Catastrophizing has been discussed as a cognitive precursor to the emergence of posttraumatic stress disorder (PTSD) symptoms following the experience of stressful events. Implicit in cognitive models of PTSD is that treatment-related reductions in catastrophizing should yield reductions in PTSD symptoms. The tenability of this prediction has yet to be tested. The present study investigated the sequential relation between changes in a specific form of catastrophizing-symptom catastrophizing-and changes in PTSD symptom severity in a sample of 73 work-disabled individuals enrolled in a 10-week behavioral activation intervention. Measures of symptom catastrophizing and PTSD symptom severity were completed at pre-, mid-, and posttreatment assessment points. Cross-sectional analyses of pretreatment data revealed that symptom catastrophizing accounted for significant variance in PTSD symptom severity, β = .40, p < .001, sr = .28 (medium effect size), even when controlling for known correlates of symptom catastrophizing, such as pain and depression. Significant reductions in symptom catastrophizing and PTSD symptoms were observed during treatment, with large effect sizes, ds = 1.42 and 0.94, respectively, ps < .001. Cross-lagged analyses revealed that early change in symptom catastrophizing predicted later change in PTSD symptoms; early changes in PTSD symptom severity did not predict later change in symptom catastrophizing. These findings are consistent with the conceptual models that posit a causal relation between catastrophizing and PTSD symptom severity. The clinical implications of the findings are discussed.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".