Strengthening mental health systems in low- and middle-income countries: recommendations from the Emerald programme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There is a large treatment gap for mental, neurological or substance use (MNS) disorders. The 'Emerging mental health systems in low- and middle-income countries (LMICs)' (Emerald) research programme attempted to identify strategies to work towards reducing this gap through the strengthening of mental health systems. AIMS: To provide a set of proposed recommendations for mental health system strengthening in LMICs. METHOD: The Emerald programme was implemented in six LMICs in Africa and Asia (Ethiopia, India, Nepal, Nigeria, South Africa and Uganda) over a 5-year period (2012-2017), and aimed to improve mental health outcomes in the six countries by building capacity and generating evidence to enhance health system strengthening. RESULTS: The proposed recommendations align closely with the World Health Organization's key health system strengthening 'building blocks' of governance, financing, human resource development, service provision and information systems; knowledge transfer is included as an additional cross-cutting component. Specific recommendations are made in the paper for each of these building blocks based on the body of data that were collected and analysed during Emerald. CONCLUSIONS: These recommendations are relevant not only to the six countries in which their evidential basis was generated, but to other LMICs as well; they may also be generalisable to other non-communicable diseases beyond MNS disorders. DECLARATION OF INTEREST: None.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it