Mental health interventions for adolescents in sub-Saharan Africa: A scoping review
Bibliographic record
Abstract
Background: Globally, adolescents are vulnerable to mental health problems, particularly those from sub-Saharan Africa (SSA) due to impoverished living conditions and a higher prevalence of chronic conditions such as HIV/AIDS in the region. The COVID-19 pandemic has further exacerbated this risk. This calls for an urgent need for evidence-based adolescent mental health interventions to reduce the risk and burden of mental health problems in SSA. The review aims to identify and characterize existing adolescent mental health interventions in SSA, as well as to evaluate their implementation strategies and effectiveness. Methods: We systematically searched PubMed, African Index Medicus, PsycINFO, Web of Science, and CINAHL databases for relevant articles. Furthermore, we searched gray literature databases, including Think Tank search, open gray, NGO search engine, and IGO search engine for additional relevant articles. The scoping review was conducted to identify original research articles on mental health interventions among adolescents in sub-Saharan Africa published from database inception to 31 December 2021. We carried out a narrative synthesis to report our findings. Results: Our literature search generated 4,750 studies, of which 1,141 were duplicates, 3,545 were excluded after screening, and 64 articles met the inclusion criteria. The 64 studies describe a total of 57 unique mental health interventions comprising 40,072 adolescents. The nature of these interventions was diverse, encompassing various implementation strategies such as economic-based, family strengthening, psychoeducation, interpersonal psychotherapy, Cognitive Behavioral Therapy, and resilience training, among others. Most of the interventions were selective interventions that targeted adolescents at high risk of developing mental health problems including adolescents living with HIV, war-affected adolescents, orphans, adolescents from poorer backgrounds, and survivors of sexual violence. Half of the interventions were delivered by lay persons. Sixty-two of the eligible studies examined the effectiveness of the mental health interventions, of which 55 of them reported a positive significant impact on various mental health outcomes. Conclusions: The review findings show that there exist several diverse interventions that promote mental health among adolescents in sub-Saharan Africa. These interventions can be implemented in diverse settings including schools, communities, health facilities, and camps, and can be delivered by lay persons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".