Expressive writing and post‐traumatic stress disorder: Effects on trauma symptoms, mood states, and cortisol reactivity
Bibliographic record
Abstract
OBJECTIVES: This study investigates the boundary conditions (feasibility, safety, and efficacy) of an expressive writing intervention for individuals with post-traumatic stress disorder [PTSD]. DESIGN: Randomized trial with baseline and 3-month follow-up measures of PTSD severity and symptoms, mood states, post-traumatic growth, and (post-only) cortisol reactivity to trauma-related stress. METHODS: Volunteers with a verified diagnosis of PTSD (N=25) were randomly assigned to an experimental group (writing about their traumatic experience) or control group (writing about time management). RESULTS: Expressive writing was acceptable to patients with PTSD and appeared safe to utilize. No changes in PTSD diagnosis or symptoms were observed, but significant improvements in mood and post-traumatic growth were observed in the expressive writing group. Finally, expressive writing greatly attenuated neuroendocrine (cortisol) responses to trauma-related memories. CONCLUSIONS: The present study provides insight into several boundary conditions of expressive writing. Writing did not decrease PTSD-related symptom severity. Although patients continue to exhibit the core features of PTSD, their capacity to regulate those responses appears improved following expressive writing. Dysphoric mood decreased after writing and when exposed to traumatic memories, participants' physiological response is reduced and their recovery enhanced.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".