The relationship between economic evaluations and HIV and AIDS treatment policies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Economics, and specifically economic evaluations, are increasingly being utilized to provide treatment policy guidance to decision makers. This article reviews work that has contributed to understanding of the relationship. RECENT FINDINGS: There is a paucity of research explicitly investigating the association between economic evaluations and HIV and AIDS treatment policy. Where it does exist, it is weak. Factors contributing to the limited impact include lack of reliable and trusted data; absence of local cost-effectiveness data for different interventions; contradictory results; challenges associated with understanding complex economic/mathematical models; inefficient implementation of HIV and AIDS policies; inability to pursue long-term health planning needs; and political will. SUMMARY: Consideration of the ways in which economic evaluations can have greater influence over HIV and AIDS policies is needed. The weak relationship between the two reflects the complicated and multifaceted decision-making process that is often influenced by socioeconomic and political factors. If an economic evaluation is to influence policy, then cognizance of this is important. Extending the economic toolkit to include broader-based models that incorporate political economy variables, but do not compromise on comprehension, validity and robustness, will offer better informed policy recommendations.
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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 | 0.017 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".