The burden and its determinants of mental health distress among adolescents dwelling in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Adolescent mental health issues are emerging as a significant public health concern across many low- and middle-income countries, particularly in Africa. This study aims to evaluate the aggregated prevalence and contributing risk factors of mental health distress among adolescents in Africa. Methodology A comprehensive search of PubMed, PsycINFO, Web of Science, Google Scholar, and HINARI databases was conducted to identify relevant articles on the prevalence and risk factors associated with mental health distress among African adolescents, published up to December 2023. The quality of the selected studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. Heterogeneity among the studies was evaluated using the I² statistical test. Potential publication bias was assessed through a funnel plot and Egger’s statistical test. This systematic review was registered with PROSPERO under reference number CRD42023494665. Results Eighteen studies encompassing data from 37,016 adolescents were included in the analysis. The overall prevalence of mental health distress among adolescents in Africa was found to be 27.34% (95% CI: 23.18–31.50). The occurrence of mental health distress is observed in older adolescents at a prevalence of 29.44% (95% CI: 23.26–35.66) and in younger adolescents at 24.73% (95% CI: 11.96–37.51). Significant risk factors identified included bullying victimization, with an odds ratio (POR) of 1.30 (95% CI: 1.16, 1.46), and experiencing hunger, with an odds ratio (POR) of 2.10 (95% CI: 1.13, 3.91). Conclusion The findings indicate a high prevalence of mental health distress among adolescents in Africa, highlighting the widespread impact on this demographic. These results underscore the urgent need for targeted interventions to prevent and address mental health distress among adolescents. Further research on a global scale is essential to develop effective prevention and treatment strategies tailored to this age group.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".