What barriers could impede access to mental health services for children and adolescents in Africa? A scoping review
Bibliographic record
Abstract
BACKGROUND: Few studies have examined the mental health needs of African children and teenagers. Based on this gap, this scoping review aims to identify barriers to mental health services, treatments and services sought, and where mental health services are received. METHODS: To pursue the stated objectives, we searched the following databases a) PsycINFO, b) CINAHL, c) Medline, and d) Web of Science. The search yielded 15,956 records in total. RESULTS: Studies included in this review were conducted in six African countries: Ethiopia, Mali, Egypt, South Africa, Nigeria, and Tunisia. The majority of the studies were conducted in South Africa (33.32%), followed by Ethiopia (25%), and Egypt (16.67%). In terms of treatments and services sought, both professional and traditional/alternative treatments were reported. The most frequently noted services were psychiatric treatments (25%), screening and diagnostic assessment (16.67%), as well as psychiatric and psychological consultations (16.67%). The most frequently reported treatment centers were psychiatric hospitals. As for treatment barriers, the three most frequently encountered barriers were: a preference for traditional/alternative and complementary treatments (33.33%), followed by stigma (25%), and a lack of knowledge/unfamiliarity with the mental health condition (25%). CONCLUSION: The results of this study are alarming due to the significant barriers to accessing mental health services coupled with the use of potentially harmful interventions to treat those mental health conditions. We hope this scoping review will help shed light on this important issue and help tomorrow's generation reach its full potential.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".