The relationship between changes in emotional intensity and treatment outcome in PTSD patients in EMDR therapy
Bibliographic record
Abstract
Background: Eye movement desensitisation and reprocessing (EMDR) therapy primarily aims to reduce the emotional intensity or subjective disturbance associated with traumatic memories. However, to date, only one study has investigated whether a reduction in emotional intensity is related to a reduction in post-traumatic stress disorder (PTSD) symptoms.Objective: Therefore, the purpose of the present study was to determine the relationship between changes in emotional intensity of traumatic memories during EMDR therapy and treatment outcomes.Method: One hundred twenty-five patients (88.8% female, M age = 36.4 years, SD = 11.40) with PTSD due to multiple traumatisation participated in a six-day intensive treatment programme consisting of a combination of six 90 min EMDR therapy sessions, six 90 min prolonged exposure sessions, six 60 min sessions of physical activity, and six 60 min psychoeducation sessions delivered at an academic outpatient mental healthcare clinic.Results: The results showed that a greater reduction in the emotional intensity of traumatic memory during EMDR therapy was associated with a larger decrease in PTSD symptoms at four weeks post-treatment.Conclusions: Clinicians should monitor changes in emotional intensity during treatment to assess treatment progress. Furthermore, the findings justify the use of memory disturbance as an outcome measure in experimental studies on EMDR therapy. Future research should focus on EMDR therapy processes and their relationship to treatment outcome, whereas replication of the present findings in other trauma populations is warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".