From Passive to Strategic Purchasing in Low and Middle Income Countries
Bibliographic record
Abstract
This chapter discusses the concept, design, implementation challenges and emerging models of successful strategic purchasing (SP) in low- and middle-income countries (L&MICs). The purchasing function is concerned with allocation and use of funds to ensure more value for the existing money by setting the right financial incentives to providers and ensuring that all individuals have access to needed health services. There is a marked difference across countries in terms of how they purchase health care. Passive purchasing implies following a predetermined budget or simply paying bills when presented. In contrast, SP involves a continuous search for the best ways to maximize health system performance by proactively answering questions such as – for whom to buy, what to buy, from whom to buy, how to pay and what impact. There are enablers and choke points for implementation of provider payment systems that need consideration. For L&MICs to do SP effectively, calls for building technical capacity in provider payment, greater policy coherence, institutional relationship between government and purchasers, and a step-by-step approach allowing countries to move towards SP.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".