Reimagining global mental health in Africa
Bibliographic record
Abstract
In 2001, the WHO launched The World Health Report most specifically addressing low-income and middle-income countries (LAMICs). It highlighted the importance of mental health (MH), identifying the severe public health impacts of mental ill health and made 10 recommendations. In 2022, the WHO launched another world MH report and reaffirmed the 10 recommendations, while concluding that 'business as usual for MH will simply not do' without higher infusions of money. This paper suggests the reason for so little change over the last 20 years is due to the importation and imposition of Western MH models and frameworks of training, service development and research on the assumption they are relevant and acceptable to Africans in LAMICs. This ignores the fact that most mental and physical primary care occurs within local non-Western traditions of healthcare that are dismissed and assumed irrelevant by Western frameworks. These trusted local institutions of healthcare that operate in homes and spiritual spaces are in tune with the lives and culture of local people. We propose that Western foundations of MH knowledge are not universal nor are their assumptions of society globally applicable. Real change in the MH of LAMICs requires reimagining. Local idioms of distress and healing, and explanatory models of suffering within particular populations, are needed to guide the development of training curricula, research and services. An integration of Western frameworks into these more successful approaches are more likely to contribute to the betterment of MH for peoples in LAMICs.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".