Associations Between Dimensions of Anxiety Sensitivity and PTSD Symptom Clusters in Active‐Duty Police Officers
Bibliographic record
Abstract
Prior studies have shown that anxiety sensitivity (AS) plays an important role in posttraumatic stress disorder (PTSD) symptom severity. The purpose of this study was to evaluate associations between empirically supported PTSD symptom clusters (i.e. reexperiencing, avoidance, numbing, hyperarousal) and AS dimensions (i.e. psychological concerns, social concerns, somatic concerns). Participants were 138 active-duty police officers (70.7% female; mean age = 38.9 years; mean time policing = 173.8 months) who, as a part of a larger study, completed measures of trauma exposure, PTSD symptoms, AS, and depressive symptoms. All participants reported experiencing at least one event that they perceived as traumatic, and 44 (31.9%) screened positive for PTSD. Officers with probable PTSD scored significantly higher on AS total as well as the somatic and psychological concerns dimensional scores than did those without PTSD. As well, a higher percentage of officers with probable PTSD scored positively on the AS-derived Brief Screen for Panic Disorder (Apfeldorf et al., 1994) compared with those without PTSD. A series of regression analyses revealed that depressive symptoms, number of reported traumas, and AS somatic concerns were significant predictors of PTSD total symptom severity as well as severity of reexperiencing. Avoidance was predicted by depressive symptoms and AS somatic concerns. Only depressive symptoms were significantly predictive of numbing and hyperarousal cluster scores. These findings contribute to understanding the nature of association between AS and PTSD symptom clusters. Implications for the treatment of individuals having PTSD with and without panic-related symptomatology 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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".