An Analysis of the Social Impacts of a Health System Strengthening Program Based on Purchasing Health Services
Bibliographic record
Abstract
Access to universal health coverage is a fundamental right that ensures that even the most disadvantaged receive health services without financial hardship. The Democratic Republic of Congo is among the poorest countries in the world, yet healthcare is primarily made by direct payment which renders care inaccessible for most Congolese. Between 2017 and 2021 a purchasing of health services initiative (Le Programme de Renforcement de l'Offre et Développement de l'accès aux Soins de Santé or PRO DS), was implemented in Kongo Central and Ituri with the assistance of the non-governmental organization Memisa Belgium. The program provided funding for health system strengthening that included health service delivery, workforce development, improved infrastructure, access to medicines and support for leadership and governance. This study assessed the social and health impacts of the PRO DS Memisa program using a health impact assessment focus. A documentary review was performed to ascertain relevant indicators of program effect. Supervision and management of health zones and health centers, use of health and nutritional services, the population's nutritional health, immunization levels, reproductive and maternal health, and newborn and child health were measured using a controlled longitudinal model. Positive results were found in almost all indicators across both provinces, with a mean proportion of positive effect of 60.8% for Kongo Central, and 70.8% in Ituri. Barriers to the program's success included the arrival of COVID-19, internal displacement of the population and resistance to change from the community. The measurable positive impacts from the PRO DS Memisa program reveal that an adequately funded multi-faceted health system strengthening program can improve access to healthcare in a low-income country such as the Democratic Republic of Congo.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".