Peritraumatic Distress and the Course of Posttraumatic Stress Disorder Symptoms: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To examine how peritraumatic distress modulates the severity of posttraumatic stress disorder (PTSD) according to the timing of the PTSD symptom assessments. METHOD: A systematic literature review of English- and French-language studies having administered the Peritraumatic Distress Inventory (PDI) was conducted. Meta-analyses were performed on correlations relating PDI and PTSD symptom scores obtained from the sampled studies. The meta-analyses, which included calculations of regression slopes, took into consideration the time at which PTSD symptoms were assessed following the traumatic event and the timing of the PDI assessment. RESULTS: The literature review yielded a total of 22 studies. The meta-analysis performed over all studies resulted in a pooled correlation coefficient of 0.55 between the PDI and PTSD symptom scores. Meta-regression analyses conducted over all data revealed no apparent decrease in the correlations according to the timing of the PTSD symptom assessments. However, there were numerical or statistically significant declines in regression slopes when the meta-regressions were separately conducted on studies having administered the PDI either within, or following, a 1-month period after a traumatic event. CONCLUSIONS: While PDI or PTSD symptom score correlations remain generally significant, they tend to decline as time elapses between the traumatic event and the PTSD assessment. This suggests there may be factors other than peritraumatic distress that increasingly account for the long-term trajectory PTSD symptoms.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".