Understanding Perceptions of Health Risk and Behavioral Responses to Air Pollution in the State of Utah (USA)
Bibliographic record
Abstract
Poor air quality in Utah creates an array of economic, environmental, and health-related impacts that merit investigation and informed political responses. Air pollution is known to cause a variety of health problems, ranging from increased rates of asthma to cardiovascular and lung disease. Our research investigates the extent of Utahn’s understanding of the health risks associated with long-term and short-term impacts of air quality. To assess the degree to which Utahn’s perceive the health risks of air pollution, we performed an ordinal logistic regression analysis using responses to the Utah Air Quality Risk and Behavioral Action Survey, a representative panel survey administered between November 2018 and January 2020 (n = 1160), to determine how socioeconomic status impacts risk perception. Socioeconomic status is not a predictor of perceiving air’s short-term risks to health. Those with more conservative political orientation, as well as those with higher religiosity scores, were less likely than those with more liberal political orientation or those with lower religiosity scores to strongly agree that air pollution poses short-term health risks. We find that for short-term health risks from air pollution, Utahns in the middle-income category are more likely than those in the low-income category to strongly agree that air pollution poses long-term health risks. In addition, those with more conservative political orientation were less likely than those with more liberal political orientation to strongly agree that air pollution poses long-term health risks.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".