Adjustments in purchasing arrangements to support the COVID-19 health sector response: evidence from eight middle-income countries
Bibliographic record
Abstract
The COVID-19 pandemic has triggered several changes in countries' health purchasing arrangements to accompany the adjustments in service delivery in order to meet the urgent and additional demands for COVID-19-related services. However, evidence on how these adjustments have played out in low- and middle-income countries is scarce. This paper provides a synthesis of a multi-country study of the adjustments in purchasing arrangements for the COVID-19 health sector response in eight middle-income countries (Armenia, Cameroon, Ghana, Kenya, Nigeria, Philippines, Romania and Ukraine). We use secondary data assembled by country teams, as well as applied thematic analysis to examine the adjustments made to funding arrangements, benefits packages, provider payments, contracting, information management systems and governance arrangements as well as related implementation challenges. Our findings show that all countries in the study adjusted their health purchasing arrangements to varying degrees. While the majority of countries expanded their benefit packages and several adjusted payment methods to provide selected COVID-19 services, only half could provide these services free of charge. Many countries also streamlined their processes for contracting and accrediting health providers, thereby reducing administrative hurdles. In conclusion, it was important for the countries to adjust their health purchasing arrangements so that they could adequately respond to the COVID-19 pandemic, but in some countries financing challenges resulted in issues with equity and access. However, it is uncertain whether these adjustments can and will be sustained over time, even where they have potential to contribute to making purchasing more strategic to improve efficiency, quality and equitable access in the long run.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".