Generating evidence to narrow the treatment gap for mental disorders in sub-Saharan Africa: rationale, overview and methods of AFFIRM
Bibliographic record
Abstract
There is limited evidence on the acceptability, feasibility and cost-effectiveness of task-sharing interventions to narrow the treatment gap for mental disorders in sub-Saharan Africa. The purpose of this article is to describe the rationale, aims and methods of the Africa Focus on Intervention Research for Mental health (AFFIRM) collaborative research hub. AFFIRM is investigating strategies for narrowing the treatment gap for mental disorders in sub-Saharan Africa in four areas. First, it is assessing the feasibility, acceptability and cost-effectiveness of task-sharing interventions by conducting randomised controlled trials in Ethiopia and South Africa. The AFFIRM Task-sharing for the Care of Severe mental disorders (TaSCS) trial in Ethiopia aims to determine the acceptability, affordability, effectiveness and sustainability of mental health care for people with severe mental disorder delivered by trained and supervised non-specialist, primary health care workers compared with an existing psychiatric nurse-led service. The AFFIRM trial in South Africa aims to determine the cost-effectiveness of a task-sharing counselling intervention for maternal depression, delivered by non-specialist community health workers, and to examine factors influencing the implementation of the intervention and future scale up. Second, AFFIRM is building individual and institutional capacity for intervention research in sub-Saharan Africa by providing fellowship and mentorship programmes for candidates in Ethiopia, Ghana, Malawi, Uganda and Zimbabwe. Each year five Fellowships are awarded (one to each country) to attend the MPhil in Public Mental Health, a joint postgraduate programme at the University of Cape Town and Stellenbosch University. AFFIRM also offers short courses in intervention research, and supports PhD students attached to the trials in Ethiopia and South Africa. Third, AFFIRM is collaborating with other regional National Institute of Mental Health funded hubs in Latin America, sub-Saharan Africa and south Asia, by designing and executing shared research projects related to task-sharing and narrowing the treatment gap. Finally, it is establishing a network of collaboration between researchers, non-governmental organisations and government agencies that facilitates the translation of research knowledge into policy and practice. This article describes the developmental process of this multi-site approach, and provides a narrative of challenges and opportunities that have arisen during the early phases. Crucial to the long-term sustainability of this work is the nurturing and sustaining of partnerships between African mental health researchers, policy makers, practitioners and international collaborators.
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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 | 0.231 | 0.285 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".