Can individuals’ beliefs help us understand nonadherence to malaria test results? Evidence from rural Kenya
Bibliographic record
Abstract
Abstract In malaria‐endemic countries about a quarter of test‐negative individuals take antimalarials (artemisinin‐based combination therapies [ACTs]). ACT overuse depletes scarce resources for subsidies and contributes to parasite resistance. As part of an experiment in Kenya that provided subsidies for rapid diagnostic test and/or for ACTs conditionally on being positive, we studied the association between beliefs on malaria status (prior and posterior the intervention) and decisions to get tested and to purchase ACTs. We find that prior beliefs do not explain the decision of getting tested (conditional on the price) and nonadherence to a negative test. However, test‐negative individuals who purchase ACTs report higher posterior beliefs than those who do not, consistent with a framework in which the formers revise beliefs upward, while the latters do not change or revise downward. We also do not find evidence that prior beliefs on ACT effectiveness and trust in test results play any major role in explaining testing or treatment behavior. Further research is needed to improve adherence to malaria‐negative test results.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".