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Record W4406850421 · doi:10.4236/psych.2025.161006

Mental Disorders in Children and Youths Aged 10 to 24 Years in the Southwest Region of Cameroon: A Cross-Sectional Analysis

2025· article· en· W4406850421 on OpenAlexfundno aff
Lifafa Kinge Kange, Eyongewube Clovert Eyong, Ayuketang Eyong Ashu, Tanyi Regobell Mua, Ashley Wotany Luma, Ghangha Jamin Ghangha, Amin Ruth Tabi, Wirnkar Jude Kanla, Vamtowe Hezal Tracy, Kum Mineva Ziagha, Tiayah Patience Foumene, Nupa Kawo Christelle

Bibliographic record

VenuePsychology · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsPsychologyCross-sectional studyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.325
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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