How People Prioritize Health Issues During the COVID-19 Pandemic:Evidence from Seven Developing Countries
Bibliographic record
Abstract
Abstract We provide estimates of health priorities during the COVID-19 pandemic based on web-surveys administered in seven developing countries in Africa, Asia, and Latin America in 2022. Using the best-worst scaling method, respondents ranked the importance of seven health problems, including COVID-19 (the others were alcohol and drugs, HIV/AIDS, malaria, TB, other respiratory diseases, and water-borne diseases). Respondents in most countries considered COVID-19 a serious problem but ranked other respiratory illness as more serious. Respondents’ rankings were generally consistent with relative disease prevalence when it can be reasonably well measured (i.e., malaria and TB). Differences in priorities across countries were generally larger than within-country differences. The importance respondents assigned to COVID-19 was associated with their knowledge of COVID-19. These results have implications for the allocation of health resources: policymakers may face resistance if their actions are viewed as focusing too much on COVID-19 while neglecting other, potentially serious health problems.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".