What drives parents’ use of air quality indexes during wildfire smoke events: predictors of index knowledge, frequent checking, and following health guidance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
= 2100) participated in an online cross-sectional study. Binary and ordinal logistic regression models were used to examine geographic, demographic, and psychosocial predictors of three dependent variables: knowledge of where to check AQI information, frequent checking of AQI information during wildfire seasons, and adherence to AQI health messages around reducing/rescheduling outdoor physical activity. Smoke-exposure analysis indicated widespread potential exposures to wildfire smoke across all four jurisdictions. Nonetheless, parents in BC, on average, were less likely to report knowing where to check AQI information, checked less frequently, and were less likely to adhere to AQI guidance than parents in the three US states. Adherence to AQI health messages did not differ by jurisdiction in the presence of other covariates, suggesting parents are equally likely to follow AQI guidance when they know where to find it and check it. Other consistent predictors of the three dependent variables included experience with prior smoke-related health impacts, smoke risk perceptions, and use of internet/mobile applications as sources of smoke information. These findings indicate that increased promotion of AQIs may benefit parents in some regions during wildfires. Future evaluations of smoke education initiatives could help health agencies share effective practices across jurisdictions and target interventions to increase AQI adoption. Supplementary information: The online version contains supplementary material available at 10.1007/s11111-025-00491-w.
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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 | 0.000 | 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 it