Implementation of mental health policies and reform in post-conflict countries: the case of post-genocide Rwanda
Bibliographic record
Abstract
The global burden of mental illness is rising, with populations in post-conflict countries contributing significantly to the numbers. Governments in these countries face the dual challenge of responding to increased mental health needs and implementing this response with institutions and economies weakened by war. This research studies the process, successes and challenges of implementing mental health reform in a low-resource, post-conflict country, a subject that is rarely studied. Based on fieldwork conducted in Rwanda in 2019, the study focuses on the implementation of this African country's post-genocide mental health policy, which relies primarily on strategies of decentralization and integration into the primary health care system. The results are based on 30 interviews conducted in Kigali and Ngoma with primary stakeholders including government officials, representatives from nongovernmental organizations, service providers and academics. These stakeholders held a positive view of the main strategies of the policy as they resulted in increased accessibility and availability of care for Rwandans. However, they also noted the institutionalization and individualization of mental health care as gaps in the implementation that do not respond to the Rwandan context. Building on complexity theory, the analysis found that many of these gaps, as well as opportunities to address them, are missed by the government due to top-down implementation and a lack of collaboration with local organizations and service providers working in the domain. The research results suggest that although it is possible to prioritize mental health in low-resource, complex settings, the implementation of such reform requires collaborative, adaptive and horizontal approaches in order to adequately address and respond to citizen needs and ensure quality mental health care for all.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".