Treatment‐related reductions in catastrophizing predict return to work in individuals with post‐traumatic stress disorder
Bibliographic record
Abstract
Abstract Post‐traumatic stress disorder (PTSD) has been associated with high rates of work‐disability. In other domains of research, it has been shown that catastrophic thinking also contributes to work‐disability. The present study examined the relation between catastrophic thinking and work‐disability in individuals with PTSD. The study sample consisted of 73 work‐disabled individuals with PTSD who were referred to an occupational rehabilitation service. Participants completed measures of post‐traumatic stress symptoms, depression, pain, catastrophic thinking, and occupational disability at admission and termination of the rehabilitation intervention. Return‐to‐work was assessed 1 month following the termination of the rehabilitation intervention. Cross‐sectional analyses revealed that catastrophic thinking contributed significant unique variance to the prediction of occupational disability, even when controlling for the severity of symptoms of PTSD. Prospective analyses revealed that treatment‐related reductions in catastrophic thinking predicted successful return to work, beyond the variance accounted for by reductions in the severity of symptoms of PTSD. The findings suggest that catastrophic thinking is a determinant of occupational disability in individuals with PTSD. The findings further suggest that interventions designed to reduce catastrophic thinking might promote more successful occupational re‐integration in individuals recovering from post‐traumatic stress symptoms.
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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 | 0.001 |
| 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".