Mental Disorders in Children and Youths Aged 10 to 24 Years in the Southwest Region of Cameroon: A Cross-Sectional Analysis
Bibliographic record
Abstract
Background: Mental health issues pose a significant threat to public health, contributing substantially to global burden of disability. In Africa, the mental health of young individuals aged 10 to 24 is increasingly at risk due to a cumulative effect of various challenges that have adversely impacted their mental wellbeing, resulting in mental disorders. This study aims at assessing some mental disorders in children and youths aged 10 to 24 years in the Southwest Region of Cameroon. Methods: A cross-sectional study over a period of 1 year involving participants aged 10 to 24 years in the Fako Division, Southwest Region of Cameroon. Consecutive sampling was used to select the participants. The data was managed using Microsoft Excel and analyzed using SPSS version 25. Results: A total of 965 participants were enrolled with more than half of the participants (522, 54.1%) showing inadequate knowledge. The overall prevalence of mental health disorder was 68.8% (substance use disorder (30.1%), depression (29.0%), anxiety (23.9%), and suicidal thoughts (19.2%). A significant association was found between mental disorders and demographic factors (age, gender, and locality, p Conclusion: Majority of the participants had inadequate knowledge on mental disorders. The overall prevalence of mental health disorders was high with significant association with gender, communities and being internally displaced. There is a need for mass sensitization, peer support and expert care to reduce the prevalence and promote mental well-being.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".