The Sub-Saharan Africa Regional Partnership (SHARP) for Mental Health Capacity Building: a program protocol for building implementation science and mental health research and policymaking capacity in Malawi and Tanzania
Bibliographic record
Abstract
Mental health (MH) disorders in low and middle-income countries (LMICs) account for a large proportion of disease burden. While efficacious treatments exist, only 10% of those in need are able to access care. This treatment gap is fueled by structural determinants including inadequate resource allocation and prioritization, both rooted in a lack of research and policy capacity. The goal of the Sub-Saharan Africa Regional Partnership for Mental Health Capacity Building (SHARP), based in Malawi and Tanzania, is to address those research and policy-based determinants.SHARP aims to (1) build implementation science skills and expertise among Malawian and Tanzanian researchers in the area of mental health; (2) ensure that Malawian and Tanzanian policymakers and providers have the knowledge and skills to effectively apply research findings on evidence-based mental health programs to routine practice; and (3) strengthen dialogue between researchers, policymakers, and providers leading to efficient and sustainable scale-up of mental health services in Malawi and Tanzania. SHARP comprises five capacity building components: introductory and advanced short courses, a multifaceted dialogue, on-the-job training, pilot grants, and "mentor the mentors" courses.Program evaluation includes measuring dose delivered and received, participant knowledge and satisfaction, as well as academic output (e.g., conference posters or presentations, manuscript submissions, grant applications). The SHARP Capacity Building Program aims to make a meaningful contribution in pursuit of a model of capacity building that could be replicated in other LMICs. If impactful, the SHARP Capacity Building Program could increase the knowledge, skills, and mentorship capabilities of researchers, policymakers, and providers regarding effective scale up of evidence-based MH treatment.
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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 | 0.164 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".