Evaluation of the Impacts of a Phone Warning and Advising System for Individuals Vulnerable to Smog. Evidence from a Randomized Controlled Trial Study in Canada
Bibliographic record
Abstract
Smog warning systems are components of adaptation strategies that are adopted by governments around the world to protect their citizens from extreme episodes of air pollution. As part of a growing research stream on the effectiveness of these systems, this article presents the results of a study on the impacts of an automated phone warning and advising system for individuals vulnerable to air pollution. A sample of 1328 individuals were recruited and randomly assigned to treatment and control groups. The treatment group received smog warning while the control group did not. Data were collected via three phone surveys, two before and one after issuing the smog warning. The comparison between treatment and control groups indicates that exposure to a smog warning improved information on the occurrence of smog episodes (n = 484, OR = 5.58, p = 0.00), and knowledge on protective behaviors. Furthermore, members of treatment group were more likely to avoid exposure to smog episodes by spending more time inside with the windows closed than usual (n = 474, OR = 2.03, p = 0.00). Members of treatment group who take medication in the form of aerosol pumps also kept these devices on themselves more frequently than those of control group (n= 109, OR = 2.15, p = 0.03). The system however had no discernible effects on the awareness of air pollution risks, reduction of health symptoms related to smog and the use of health system services. The absence of health benefits could be related to the lower actual exposure to air pollution of such vulnerable groups during winter.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".