Increasing the priority of mental health in Africa: findings from qualitative research in Ghana, South Africa, Uganda and Zambia
Bibliographic record
Abstract
Despite the high prevalence of mental illness, mental health remains a low priority in Africa. There has been no investigation of the views of stakeholders in Africa on why this is and what can be done. This paper reports a comparison of the views of stakeholders in Ghana, South Africa, Uganda and Zambia, focusing on the priority given to mental health by the government at the national and regional/province levels. We conducted semi-structured interviews with key stakeholders and used a two-stage approach to analysis: firstly framework analysis in each study country, followed by comparative analysis of the country data. Mental health was largely considered a low priority at national and regional/provincial levels in all four countries. We identified nine factors affecting the priority of mental health, which were grouped into three categories: legitimacy of the problem, feasibility of response and support for response. Respondents put forward a range of experiences and suggestions for increasing the priority given to mental health. We conclude with broad suggestions to raise the priority of mental health. These suggestions are particularly relevant as mental health increases in priority on the international agenda, in order to inform advocacy for increased priority for mental health in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".