Improving social accountability processes in the health sector in sub-Saharan Africa: a systematic review
Bibliographic record
Abstract
BACKGROUND: Social accountability is a participatory process in which citizens are engaged to hold politicians, policy makers and public officials accountable for the services that they provide. In the Fifteenth Ordinary Session of the Assembly of the African Union, African leaders recognized the need for strong, decentralized health programs with linkages to civil society and private sector entities, full community participation in program design and implementation, and adaptive approaches to local political, socio-cultural and administrative environments. Despite the increasing use of social accountability, there is limited evidence on how it has been used in the health sector. The objective of this systematic review was to identify the conditions that facilitate effective social accountability in sub-Saharan Africa. METHODS: Electronic databases (MEDLINE, PsycINFO, Sociological Abstracts, Social Sciences Abstracts) were searched for relevant articles published between 2000 and August 2017. Studies were eligible for inclusion if they were peer-reviewed English language publications describing a social accountability intervention in sub-Saharan Africa. Qualitative and quantitative study designs were eligible. RESULTS: Fourteen relevant studies were included in the review. The findings indicate that effective social accountability interventions involve leveraging partnerships and building coalitions; being context-appropriate; integrating data and information collection and analysis; clearly defined roles, standards, and responsibilities of leaders; and meaningful citizen engagement. Health system barriers, corruption, fear of reprisal, and limited funding appear to be major challenges to effective social accountability interventions. CONCLUSION: Although global accountability standards play an important guiding role, the successful implementation of global health initiatives depend on national contexts.
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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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".