Community Perceptions of Air Pollution and Related Health Risks in Nairobi Slums
Bibliographic record
Abstract
Air pollution is among the leading global risks for mortality and responsible for increasing risk for chronic diseases. Community perceptions on exposure are critical in determining people's response and acceptance of related policies. Therefore, understanding people' perception is critical in informing the design of appropriate intervention measures. The aim of this paper was to establish levels and associations between perceived pollution and health risk perception among slum residents. A cross-sectional study of 5,317 individuals aged 35+ years was conducted in two slums of Nairobi. Association of perceived score and individual characteristics was assessed using linear regression. Spatial variation in the perceived levels was determined through hot spot analysis using ArcGIS. The average perceived air pollution level was higher among residents in Viwandani compared to those in Korogocho. Perceived air pollution level was positively associated with perceived health risks. The majority of respondents were exposed to air pollution in their place of work with 66% exposed to at least two sources of air pollution. Less than 20% of the respondents in both areas mentioned sources related to indoor pollution. The perceived air pollution level and related health risks in the study community were low among the residents indicating the need for promoting awareness on air pollution sources and related health risks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".