Allocating external financing for health: a discrete choice experiment of stakeholder preferences
Bibliographic record
Abstract
Most donors of external financing for health use allocation policies to determine which countries are eligible to receive financial support and how much support each should receive. Currently, most of these policies place a great deal of weight on income per capita as a determinant of aid allocation but there is increasing interest in putting more weight on other country characteristics in the design of such policies. It is unclear, however, how much weight should be placed on other country characteristics. Using an online discrete choice experiment designed to elicit preferences over country characteristics to guide decisions about the allocation of external financing for health, we find that stakeholders assign a great deal of importance to health inequalities and the burden of disease but put very little weight on income per capita. We also find considerable variation in preferences across stakeholders, with people from low- and middle-income countries putting more weight on the burden of disease and people from high-income countries putting more weight on health inequalities. These findings suggest that stakeholders put more weight on burden of disease and health inequalities than on income per capita in evaluating which countries should received external financing for health and that that people living in aid recipient may have different preferences than people living in donor countries. Donors may wish to take these differences in preferences in mind if they are reconsidering their aid allocation policies.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".