Clarifying the Relationship Between AS Dimensions and PTSD Symptom Clusters: Are Negative and Positive Affectivity Theoretically Relevant Constructs?
Bibliographic record
Abstract
The association between anxiety sensitivity (AS) and posttraumatic stress disorder (PTSD) has been established in contemporary literature; however, research is divided over the nature of specific relationships between AS dimensions and PTSD symptoms clusters. Further, a paucity of research has examined the AS and PTSD relationship while accounting for theoretically relevant variables, such as negative (NA) and positive affect (PA). The purpose of the current study was twofold: first, to clarify divergent findings regarding the contribution of AS dimensions to PTSD symptom clusters, and, second, to further assess the relevance of NA and PA within the AS/PTSD relationship. Hierarchal regression analyses showed that, beyond shared variance attributable to NA and PA, AS somatic concerns were significantly associated with three of four PTSD symptom (i.e., reexperiencing, numbing, hyperarousal), AS cognitive concerns were only associated with hyperarousal, and AS socially observable symptoms were not significantly associated with any PTSD symptom clusters. These findings suggest that AS somatic concerns are the most robust predictor of variance within the AS/PTSD relationship and further clarify the theoretical importance of NA and PA within this relationship. Comprehensive results, implication, and directions for future research 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".