Anxiety sensitivity and posttraumatic stress symptoms among public safety personnel: A longitudinal analysis.
Bibliographic record
Abstract
OBJECTIVE: Public safety personnel (PSP) are likely to encounter multiple traumatic events and are thus more likely to develop posttraumatic stress disorder than the general population. It is important, then, to identify modifiable factors that increase the risk for posttraumatic stress disorder so that preventative treatments may be developed. Anxiety sensitivity (AS; the fear of physiological arousal sensations) has been identified as one such potential modifiable risk factor. However, research on the longitudinal, reciprocal association between AS and posttraumatic stress symptoms (PTSS) in those experiencing multiple traumas is limited. The aim of the present study was to investigate the association between AS and PTSS in PSP over the course of 1 year. METHOD: = 272) recruited via social media completed self-report measures of PTSS, trauma exposure, and AS at baseline, 6 months, and 1 year. RESULTS: Cross-lagged panel analysis did not reveal cross-lagged associations between AS and PTSS. Instead, only Wave 2 trauma exposure negatively predicted Wave 3 PTSS. CONCLUSIONS: Findings may be due to strong autoregressive associations (e.g., baseline PTSS predicting future PTSS) or may indicate that the association between AS and PTSS is more difficult to disentangle in populations that have already experienced multiple traumatic events. Future research should investigate this association among PSP who have yet to be exposed to potentially traumatic events to further elucidate whether AS is a risk factor worth intervention (or not) in this population. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".