Posttraumatic Stress Symptoms and the Diathesis-Stress Model of Chronic Pain and Disability in Patients Undergoing Major Surgery
Bibliographic record
Abstract
OBJECTIVES: To (1) use structural equation modeling (SEM) to examine relationships proposed in Turk's diathesis-stress model of chronic pain and disability as well as (2) investigate what role, if any, posttraumatic stress symptoms (PTSS) play in predicting pain disability, relative to some of the other factors in the model. METHODS: The study sample consisted of 208 patients scheduled for general surgery, 21 to 60 years of age (mean age=47.18 y, SD=9.72 y), who reported experiencing persistent pain for an average of 5.56 years (SD=7.90 y). At their preadmission hospital visit, patients completed the Anxiety Sensitivity Index, Pain Catastrophizing Scale, Pain Anxiety Symptoms Scale-20, Pain Disability Index, posttraumatic stress disorder Checklist, and rated the average intensity of their pain (0 to 10 numeric rating scale). SEM was used to test a model of chronic pain disability and to explore potential relationships between PTSS and factors in the diathesis-stress model. RESULTS: SEM results provided support for a model in which anxiety sensitivity predicted fear of pain and catastrophizing, fear of pain predicted escape/avoidance, and escape/avoidance predicted pain disability. Results also provided support for a feedback loop between disability and fear of pain. SEM analyses provided preliminary support for the inclusion of PTSS in the diathesis-stress model, with PTSS accounting for a significant proportion of the variance in pain disability. DISCUSSION: Results provide empirical support for aspects of Turk's diathesis-stress model in a sample of patients with persistent pain. Findings also offer preliminary support for the role of PTSS in fear-avoidance models of chronic pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".