The use of CBT (Cognitive Behavioural Therapy) as a treatment of post-traumatic stress disorder among adolescents in Nigeria
Bibliographic record
Abstract
Background: Post-traumatic stress disorder (PTSD) is a significant mental health concern among adolescents in Nigeria, exacerbated by socio-economic challenges and violence. Despite its high prevalence, treatment options remain limited, with Cognitive Behavioral Therapy (CBT) emerging as a promising intervention. Aim: To evaluate the efficacy of Cognitive Behavioral Therapy in treating PTSD among adolescents in Nigeria. Setting: Multiple regions across Nigeria, considering local socio-cultural factors influencing mental health outcomes. Methods: A systematic review was conducted across PubMed, Cochrane Library, Web of Science, and CINAHL Complete databases (up to March 2024). The search combined terms related to "PTSD," "CBT," and "cognitive psychotherapy" using Boolean operators (AND and OR). Results: Findings indicate that CBT significantly reduces PTSD symptoms in Nigerian adolescents, particularly anxiety, depression, and social withdrawal. The therapy's structured problem-solving techniques were shown to be effective in alleviating stressors unique to this demographic. Studies highlighted improvements in emotional regulation and coping mechanisms, suggesting CBT's adaptability to the Nigerian context. Conclusion: CBT is an effective therapeutic intervention for managing PTSD among adolescents in Nigeria, demonstrating substantial improvements in mental health outcomes. Its structured approach provides a feasible solution to address the high burden of PTSD in low-resource settings. Contribution: This study contributes to the limited body of knowledge on PTSD interventions in Nigeria, emphasizing the potential of CBT to enhance adolescent mental health in diverse socio-cultural environments. The findings support the integration of CBT into mental health policies to address adolescent PTSD in Nigeria.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".