Trauma‐focused cognitive behavioral therapy for children and youth in low and middle‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND: Trauma-focused cognitive behavioral therapy (TF-CBT) has been identified as a gold standard treatment for childhood posttraumatic stress disorder (PTSD) in Western countries. More recently, TF-CBT has emerged in the literature as an area of interest for children and youth affected by conflict and war in low- and middle-income countries (LMIC). METHODS: The present systematic review assesses the current evidence base of TF-CBT for children and youth in LMIC, with a focus on conflict-affected countries. A total of 143 articles were identified, of which 11 articles, representing 1,354 participants, met the proposed inclusion criteria. RESULTS: Results showed that the majority of the studies identified were conducted in low-resource community settings in East or Central Africa (n = 8). It was also found that cultural considerations were taken into account in TF-CBT delivery to meet the needs of local populations. Additionally, measures were translated and validated for local use. CONCLUSIONS: Findings of outcome data indicated that TF-CBT was effective in treating trauma-related symptoms and improving psychosocial functioning in children and adolescents in LMICs. Given the limited number of published literature available in this area, further studies are needed to conclude when and for whom trauma-focused interventions are most relevant.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".