Strengthening mental health systems in low- and middle-income countries: recommendations from the Emerald programme
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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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".