Accountability in malaria prevention and treatment programmes: a review of current challenges
Bibliographic record
Abstract
Background Sound governance is a fundamental tenet of financial aid programmes for malaria prevention and treatment in low-income countries, yet their limited effectiveness in reducing the prevalence of malaria suggests weaknesses in the underlying accountability frameworks. Despite heightened global attention since the Roll Back Malaria Partnership was launched in 1998, 214 million new cases and 438 000 deaths due to malaria were reported in 2015. Millions of people lack access to malaria prevention and treatment services in Africa, where 80% of malaria deaths occur. We aimed to clarify accountability frameworks and mechanisms that underlie malaria prevention and treatment programmes in sub-Saharan African countries. Methods To clarify accountability mechanisms in maternal and child health malaria programmes in Benin, Burkina Faso, and Mali, we reviewed policy reports and studies published between Jan 1, 2000, and Aug 1, 2016, and undertook semi-structured key informant interviews with national ministries of health, non-governmental organisations, WHO, and the Global Fund to fight AIDS, Tuberculosis and Malaria. We triangulated results with findings from interviews with local health professionals and pregnant women done in 2015–16. We analysed recorded and transcribed interviews through framework analysis using NVivo software. Findings We included 15 reports, 60 studies, and 118 individual interviews in analysis. Our analysis of accountability frameworks identified general indicators of programme implementation success such as antenatal physician visit attendance. However, important limitations in programmes were also identified. These included insufficient delineation and measurement of operational aspects of programme implementation and a lack of specific measures within local health systems used to ensure target groups' access to malaria prevention and treatment. The manner in which preventable health system deficiencies, such as medication shortages, were to be addressed and evaluated were absent from current accountability frameworks. We also noted that global accountability measures were "glocalised" by national actors, since policy adoption was infused with local customs and practices. Interpretation National health leadership is a key driver in successful health outcomes. Changes in accountability frameworks to foster sound national health governance and leadership could support development of a more comprehensive array of practices to specifically address challenges in programme implementation, including how preventable local health system deficiencies will be alleviated to meet programme goals. Country-specific programme goals should be harmonised with the goals of national health leaders. Funding York University, ON, Canada.
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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 | 0.033 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".