Building a middle-range theory of free public healthcare seeking in sub-Saharan Africa: a realist review
Bibliographic record
Abstract
Realist reviews are a new form of knowledge synthesis aimed at providing middle-range theories (MRTs) that specify how interventions work, for which populations, and under what circumstances. This approach opens the 'black box' of an intervention by showing how it triggers mechanisms in specific contexts to produce outcomes. We conducted a realist review of health user fee exemption policies (UFEPs) in sub-Saharan Africa (SSA). This article presents how we developed both the intervention theory (IT) of UFEPs and a MRT of free public healthcare seeking in SSA, building on Sen's capability approach. Over the course of this iterative process, we explored theoretical writings on healthcare access, services use, and healthcare seeking behaviour. We also analysed empirical studies on UFEPs and healthcare access in free care contexts. According to the IT, free care at the point of delivery is a resource allowing users to make choices about their use of public healthcare services, choices previously not generally available to them. Users' ability to choose to seek free care is influenced by structural, local, and individual conversion factors. We tested this IT on 69 empirical studies selected on the basis of their scientific rigor and relevance to the theory. From that analysis, we formulated a MRT on seeking free public healthcare in SSA. It highlights three key mechanisms in users' choice to seek free public healthcare: trust, risk awareness and acceptability. Contextual elements that influence both users' ability and choice to seek free care include: availability of and control over resources at the individual level; characteristics of users' and providers' communities at the local level; and health system organization, governance and policies at the structural level.
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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 | 0.030 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".