Bibliographic record
Abstract
This paper documents the quality of medical advice in low-income countries. Our evidence on health care quality in low-income countries is drawn primarily from studies in four countries: Tanzania, India, Indonesia, and Paraguay. We provide an overview of recent work that uses two broad approaches: medical vignettes (in which medical providers are presented with hypothetical cases and their responses are compared to a checklist of essential procedures) and direct observation of the doctor--patient interaction These two approaches have proved quite informative. For example, doctors in Tanzania complete less than a quarter of the essential checklist for patients with classic symptoms of malaria, a disease that kills 63,000-96,000 Tanzanians each year. A public-sector doctor in India asks one (and only one) question in the average interaction: "What's wrong with you?" We present systematic evidence in this paper to show that these isolated facts represent common patterns. We find that the quality of care in low-income countries as measured by what doctors know is very low, and that the problem of low competence is compounded due to low effort--doctors provide lower standards of care for their patients than they know how to provide. We discuss how the properties and correlates of measures based on vignettes and observation may be used to evaluate policy changes. Finally, we outline the agenda in terms of further research and measurement.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.110 | 0.018 |
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".