A Little Planning Goes a Long Way: Multilevel Allocation of HIV Prevention Resources
Bibliographic record
Abstract
BACKGROUND: HIV prevention funds are often allocated by decision makers at multiple levels. High-level decision makers may allocate funds to regions, and regional decision makers then allocate those funds to specific programs. Often, funds are allocated proportionally (e.g., in proportion to HIV incidence) rather than efficiently (i.e., to maximize HIV infections averted). The authors investigate the impact of efficient and proportional allocation methods at 2 different decision levels. METHODS: The authors developed an optimization model of resource allocation at 2 levels-an aggregate upper level and multiple local levels-and considered efficient allocation and allocation proportional to HIV incidence. Using data from 40 U.S. states, they compared 4 strategies for allocating HIV prevention funds. RESULTS: The greatest health benefit (HIV infections averted) occurred when efficient allocations were made at both levels. When funds were allocated proportionally at the higher level and efficiently at the lower level, the health benefit was about 5% less than when efficient allocations were made at both levels. When funds were allocated efficiently at the higher level and proportionally at the lower level, the health benefit was 15% less than when efficient allocations were made at both levels. The least health benefit (23% less than when efficient allocations were made at both levels) occurred with proportional allocation at both levels. CONCLUSIONS: Efficient allocation only at the higher level cannot overcome poor allocations at lower levels. Moreover, efficient allocation at the lower level is likely to yield greater gains than efficient allocation at the higher level. Thus, upper-level decision makers, such as donor organizations, should develop incentives to promote efficient allocation by lower-level decision makers.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".