Infrastructure Provisioning and Health Service Utilization in Africa
Bibliographic record
Abstract
Although the positive developmental effects of infrastructure provisioning are well documented, research on the potential role of governance in the improvement of infrastructure performance and individual-level service utilization is lacking. I explore the effect of infrastructure provisioning on individual-level health service utilization, paying close attention to whether governance at different levels shapes people's access to health care. The different geographical levels of infrastructure provisioning, governance, and health service utilization require a multilevel analysis, which I perform using Afrobarometer Round 5 survey data on 34 African countries in a three-stage mixed-effects modeling. Results show that the presence of health infrastructure is crucial for enhancing people's health service utilization. However, people encounter certain problems when receiving services at their local health clinics or hospitals, and these problems are directly linked with governance in the health sector as well as overall governance at the country level. Improvements in people's health service utilization therefore require both better infrastructure provisioning and better governance at different levels, as the former does not guarantee the latter. Development scholars need to widen their focus beyond national-level governance and help policy makers identify at which level state interventions are most needed for removing barriers to development.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".