Do radon risk maps encourage residential testing behaviour? Evidence from an experimental study in Canada
Bibliographic record
Abstract
OBJECTIVES: Radon is a leading cause of lung cancer and poses an even greater risk for people who smoke. Maps are used to educate the public about radon and promote testing, yet their effectiveness is rarely evaluated. This study investigated the effects of a radon risk map-in collaboration with a provincial health agency-on testing intentions. An exploratory aim was to assess potential mechanisms, applying psychological insights on risk perception, to evaluate how maps function as risk communication tools. STUDY DESIGN: Online experiment conducted in December 2023 with a quota-sampled panel of residents. METHODS: 1716 British Columbia residents completed measures of radon risk perceptions and testing intentions before and after viewing a risk map adapted from the British Columbia Centre for Disease Control. All participants viewed a pre-experiment prompt on the importance of protecting against radon, especially if you smoke/have smoked. Multilevel regression models assessed changes in testing intentions across subgroups. Path analysis tested worry as a mediator, controlling for demographic covariates. RESULTS: Participants in medium-high- or high-radon-risk areas reported significantly higher average radon testing intentions post-experiment (p < 0.001), irrespective of smoking status. A small decrease was observed among non-smokers living in the lowest ecological risk areas (p < 0.001). Changes in worry appeared to mediate the association between participants' radon risk level and testing intentions. CONCLUSION: Results suggest maps can influence perceptions of risk and encourage radon testing in high-risk areas. In low-risk areas, communications emphasizing the severity of lung cancer and its occurrence in non-smokers may enhance public education and promote testing.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".