The Peritraumatic Distress Inventory: A Proposed Measure of PTSD Criterion A2
Bibliographic record
Abstract
OBJECTIVE: Meeting criterion A2 for the diagnosis of posttraumatic stress disorder (PTSD) in DSM-IV requires that an individual have high levels of distress during or after the traumatic event. Because of the paucity of valid and reliable instruments for assessing such responses, the authors developed a 13-item self-report measure, the Peritraumatic Distress Inventory, to obtain a quantitative measure of the level of distress experienced during and immediately after a traumatic event. METHOD: The cross-sectional study group comprised 702 police officers and 301 matched nonpolice comparison subjects varying in ethnicity and gender who were exposed to a wide range of critical incidents. RESULTS: The Peritraumatic Distress Inventory was found to be internally consistent, with good test-retest reliability and good convergent and divergent validity. Even after controlling for peritraumatic dissociation and for general psychopathology, the authors found that Peritraumatic Distress Inventory scores correlated with two measures of posttraumatic stress symptoms. CONCLUSIONS: The Peritraumatic Distress Inventory holds promise as a measure of PTSD criterion A2. Future studies should prospectively examine the ability of the Peritraumatic Distress Inventory to predict PTSD and its associated biological and cognitive correlates in other trauma-exposed groups.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".