Reducing analogue trauma symptoms by computerized reappraisal training – Considering a cognitive prophylaxis?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Distressing intrusions are a hallmark of posttraumatic stress disorder (PTSD). Dysfunctional appraisal of these symptoms may exacerbate the disorder, and conversely may lead to further intrusive memories. This raises the intriguing possibility that learning to 'reappraise' potential symptoms more functionally may protect against such symptoms. Woud, Holmes, Postma, Dalgleish, and Mackintosh (2012) found that 'reappraisal training' when delivered after an analogue stressful event reduced later intrusive memories and other posttraumatic symptoms. The present study aimed to investigate whether reappraisal training administered before a stressful event is also beneficial. METHODS: Participants first received positive or negative reappraisal training (CBM-App training) using a series of scripted vignettes. Subsequently, participants were exposed to a film with traumatic content. Effects of the CBM-App training procedure were assessed via three distinct outcome measures, namely: (a) post-training appraisals of novel ambiguous vignettes, (b) change scores on the Post Traumatic Cognitions Inventory (PTCI), and (c) intrusive symptom diary. RESULTS: CBM-App training successfully induced training-congruent appraisal styles. Moreover, those trained positively reported less distress arising from their intrusive memories of the trauma film during the subsequent week than those trained negatively. However, the induced appraisal bias only partly affected PTCI scores. LIMITATIONS: Participants used their own negative event as a reference for the PTCI assessments. The events may have differed regarding their emotional impact. There was no control group. CONCLUSIONS: CBM-App training has also some beneficial effects when applied before a stressful event and may serve as a cognitive prophylaxis against trauma-related symptomatology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".