Residual posttraumatic stress disorder symptoms after provision of brief behavioral intervention in low‐ and middle‐income countries: An individual‐patient data meta‐analysis
Bibliographic record
Abstract
BACKGROUND: To address shortages of mental health specialists in low- and middle-income countries, task-shifting approaches have been employed to train nonspecialists to deliver evidence-based scalable psychosocial interventions. Problem Management Plus (PM+) is a brief transdiagnostic nontrauma focused intervention for people affected by adversity. This study reports on the capacity of PM+ to address specific symptoms of posttraumatic stress disorder (PTSD). METHODS: Individual patient data from three randomised controlled trials were combined and analysed to observe the impacts of PM+ (n = 738) or enhanced treatment as usual (ETAU) (n = 742) interventions on specific PTSD symptoms at posttreatment and 3-month follow-up. The PTSD-Checklist for DSM-5 (PCL-5) was used to index PTSD symptoms, and presence of each symptom was defined as moderate severity (score ≥ 2 on individual items). RESULTS: The average PCL-5 score at baseline was 26.1 (SD: 16.8) with 463 (31.3%) scoring above 33, indicative of a diagnosis of PTSD. Following intervention, 12.5% and 5.8% of participants retained a score greater than 33 at postassessment and follow-up, respectively. There was greater symptom reduction for PM+ than for ETAU for most symptoms. Hyperarousal symptoms were the most common residual symptoms after PM+, with more than 30% of participants reporting persistent sleep disturbance, concentration difficulties, and anger. CONCLUSION: PM+ led to greater reduction in symptoms relating to re-experiencing and avoidance. The evidence indicates that strategies focusing on hyperarousal symptoms including sleep, concentration, and anger difficulties, could be strengthened in this brief intervention.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".