Meeting the mental health needs of low- and middle-income countries: the start of a long journey
Bibliographic record
Abstract
SUMMARY: Mental health is increasingly recognised as an important component of global health. In recognition of this fact, the European Union funded the Emerald programme (Emerging Mental Health Systems in Low- and Middle-Income Countries). The aims were to improve mental health in the following six low- and middle-income countries (LMICs): Ethiopia, India, Nepal, Nigeria, South Africa and Uganda. The Emerald programme offers valuable insights into addressing the mental health needs of LMICs. It provides a framework and practical tools. However, it will be important to evaluate longer-term effects including improvements in mental health outcomes, as well as the applicability to LMICs beyond existing participant countries. Importantly, this must be coupled with efforts to improve health worker retention in LMICs.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".