Evaluating the complex interactions between malaria and cholera prevalence, neglected tropical disease comorbidities, and community perception of health risks of climate change
Bibliographic record
Abstract
The burgeoning literature on the climate change–human health nexus has focused almost exclusively on the health impacts of climate change with little attention to how ill-health and disease influence public perception of the health risks of climate change. Based on a cross-sectional survey of 1,253 individuals, linear regression was used to examine the independent effects of malaria and cholera prevalence, and neglected tropical disease comorbidities on perceived health risks of climate change. Individuals who reported more comorbidities had higher scores on perceived health risks of climate change compared with those who did not report any comorbidities. Unexpectedly, at the multivariate level, there were no statistically significant relationships between age of respondents, gender, and educational attainment on the one hand, and perceived health risks of climate change on the other hand. Individuals who were diagnosed with cholera in the past 12 months had higher scores on perceived health risks of climate change but there was no relationship between diagnosis with malaria in the past 12 months and perceived health risks. Individuals who had attained secondary education had lower scores on perceived health risks of climate change compared with those without any formal education. Given that this relationship did not exist at the bivariate level, it indicates that biosocial and sociocultural factors suppressed the relationship between secondary education attainment and perceived health risks of climate change. The findings underscore the complex relationship between perceived health risks of climate change and infectious disease, comorbidities, compositional, and contextual factors at the multivariate level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".