A Narrative Synthesis Review of Out-of-Pocket Payments for Health Services Under Insurance Regimes: A Policy Implementation Gap Hindering Universal Health Coverage in Sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: "Achieve universal health coverage (UHC), including financial risk protection, access to quality essential healthcare services and access to safe, effective, quality and affordable essential medicines and vaccines for all" is the Sustainable Development Goal (SDG) 3.8 target. Although most high-income countries have achieved or are very close to this target, low- and middle-income countries (LMICs) especially those in sub-Saharan Africa (SSA) are still struggling with its achievement. One of the observed challenges in SSA is that even where services are supposed to be "free" at point-of-use because they are covered by a health insurance scheme, out-of-pocket fees are sometimes being made by clients. This represents a policy implementation gap. This study sought to synthesise the known evidence from the published literature on the 'what' and 'why' of this policy implementation gap in SSA. METHODS: The study drew on Lipsky's street level bureaucracy (SLB) theory, the concept of practical norms, and Taryn Vian's framework of corruption in the health sector to explore this policy implementation gap through a narrative synthesis review. The data from selected literature were extracted and synthesized iteratively using a thematic content analysis approach. RESULTS: Insured clients paid out-of-pocket for a wide range of services covered by insurance policies. They made formal and informal cash and in-kind payments. The reasons for the payments were complex and multifactorial, potentially explained in many but not all instances, by coping strategies of street level bureaucrats to conflicting health sector policy objectives and resource constraints. In other instances, these payments appeared to be related to structural violence and the 'corruption complex' governed by practical norms. CONCLUSION: A continued top-down approach to health financing reforms and UHC policy is likely to face implementation gaps. It is important to explore bottom-up approaches - recognizing issues related to coping behaviour and practical norms in the face of unrealistic, conflicting policy dictates.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".