COGNITIVE FACTORS IN TRAUMATIC STRESS REACTIONS: PREDICTING PTSD SYMPTOMS FROM ANXIETY SENSITIVITY AND BELIEFS ABOUT HARMFUL EVENTS
Bibliographic record
Abstract
The present study evaluated the relative importance of different cognitive factors (anxiety sensitivity and trauma-related beliefs) in predicting PTSD symptom severity and treatment-related changes in these symptoms. Eighty-one victims of motor vehicle accidents (MVAs) completed self-report measures of PTSD symptoms, anxiety sensitivity (AS), MVA-related beliefs and control variables (e.g., medication use, pain severity). A subsample of patients (n=28), who received cognitive-behavioural treatment for PTSD, completed these measures pre- and post-treatment. For the combined sample (n=81), regression analyses indicated that AS and pain severity were significant predictors of PTSD symptoms, whereas MVA-related beliefs were not. For patients completing treatment, regression analyses indicated that reductions in AS and pain severity were significant predictors of reductions in PTSD symptoms. MVA-related beliefs did not significantly predict symptom reduction once AS, pain severity and medication status was controlled for. These findings suggest that AS is a significant cognitive risk factor for exacerbating and maintaining PTSD symptoms. Treatment implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".