Progress and challenges in implementation of the WHO special initiative on mental health in Ghana (2019 - 2023): A narrative review
Bibliographic record
Abstract
Background: The WHO Special Initiative for Universal Mental Health (2019-2023) seeks to expand access to affordable, high-quality mental health services for 100 million people across 12 priority countries, including Ghana. Despite the ongoing efforts, Ghana continues to face a substantial treatment gap of approximately 98%, with limited service coverage. Objective: This review article aims to synthesise the literature on the implementation of the WHO Special Initiative in Ghana, evaluating its progress, highlighting challenges, and offering recommendations to strengthen the country’s mental health system. Methods: This review analysed peer-reviewed and grey literature, policy documents, and reports on the WHO’s Special Initiative for Universal Mental Health. Sources published in English upto 2023 were retrieved from major databases and key institutional websites. Data were synthesised thematically, guided by the WHO Health System Building Blocks Framework. Results: Ghana has made notable progress under the WHO’s Special Initiative for Universal Mental Health. Key achievements include the Ministry of Health approval of a comprehensive essential health services package which includes mental health, the establishment of the Mental Health Board for service delivery, and the inclusion of selected mental health conditions in the National Health Insurance Scheme (NHIS) benefits. Despite these significant gains, critical challenges remain, including a limited workforce, low accessibility and treatment coverage, persistent stigma and discrimination, limited funding, shortages and non-adherence to psychotropic medications, limited availability of psychosocial therapies, and ongoing human rights violations. Conclusion: Despite Ghana’s notable advancements in strengthening its mental health system, the targets of the WHO’s Special Initiative remain unmet due to complex systemic and structural challenges. Achievement of these remaining targets requires multifaceted interventions, including expanding the workforce, improving access and coverage, addressing stigma and discrimination, securing sustainable funding, ensuring availability and adherence to psychotropic medications, scaling up psychosocial services, and addressing sociocultural, religious, and human rights concerns. These findings outline actionable steps toward universal mental health coverage.
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 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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 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".