AIP-based Professional Intervention Program for Adversity for trauma and stress reduction in groups: a pilot study in Ethiopia
Bibliographic record
Abstract
Introduction: Drawing from the principles of EMDR (Eye Movement Desensitization and Reprocessing) therapy and the AIP model, the Professional Intervention Program for Adversity (PIPA) was developed with the objective of amalgamating low-intensity group exercises into a unified framework, as a comprehensive intervention for group therapy. The PIPA Program integrates various aspects of EMDR therapy-such as stabilization, resourcing, desensitization, reprocessing, and forming beliefs about the self and future-into a cohesive program. The program's structure includes self-regulation exercises, the Pillars of Life, the Flash Technique, and the Quadrants exercise. Methods: The PIPA Program was administered to more than 220 individuals with a high probability of traumatization by the two-year civil war in Ethiopia (2020-2022). Results: The results of this study show a statistically significant improvement in PTSD symptoms on PCL-5 scores (from M = 38.58 to M = 20.59) after completing the entire PIPA Program and statistically significant lower SUDS scores within the program segments of the Flash Technique and the Quadrants exercises. Discussion: Future studies should explore the long-term effects of the PIPA Program and its broader application across different therapeutic contexts. The findings suggest that the PIPA Program is a promising group-based intervention for trauma treatment that is safe and effective, especially in non-clinical settings and for culturally diverse populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".