Psychological Trauma Symptom Improvement in Veterans Using Emotional Freedom Techniques
Bibliographic record
Abstract
This study examined the effect of Emotional Freedom Techniques (EFT), a brief exposure therapy combining cognitive and somatic elements, on posttraumatic stress disorder (PTSD) and psychological distress symptoms in veterans receiving mental health services. Veterans meeting the clinical criteria for PTSD were randomized to EFT (n = 30) or standard of care wait list (SOC/WL; n = 29). The EFT intervention consisted of 6-hour-long EFT coaching sessions concurrent with standard care. The SOC/WL and EFT groups were compared before and after the intervention (at 1 month for the SOC/WL group and after six sessions for the EFT group). The EFT subjects had significantly reduced psychological distress (p < 0.0012) and PTSD symptom levels (p < 0.0001) after the test. In addition, 90% of the EFT group no longer met PTSD clinical criteria, compared with 4% in the SOC/WL group. After the wait period, the SOC/WL subjects received EFT. In a within-subjects longitudinal analysis, 60% no longer met the PTSD clinical criteria after three sessions. This increased to 86% after six sessions for the 49 subjects who ultimately received EFT and remained at 86% at 3 months and at 80% at 6 months. The results are consistent with that of other published reports showing EFT's efficacy in treating PTSD and comorbid symptoms and its long-term effects.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".