The efficacy of public health information for encouraging radon gas awareness and testing varies by audience age, sex and profession
Bibliographic record
Abstract
Radioactive radon inhalation is a leading cause of lung cancer and underlies an ongoing public health crisis. Radon exposure prevention strategies typically begin by informing populations about health effects, and their initial efficacy is measured by how well and how fast information convinces individuals to test properties. This communication process is rarely individualized, and there is little understanding if messages impact diverse demographics equally. Here, we explored how 2,390 people interested in radon testing differed in their reaction to radon's public health information and their subsequent decision to test. Only 20% were prompted to radon test after 1 encounter with awareness information, while 65% required 2-5 encounters over several months, and 15% needed 6 to > 10 encounters over many years. People who most delayed testing were more likely to be men or involved in engineering, architecture, real estate and/or physical science-related professions. Social pressures were not a major factor influencing radon testing. People who were the least worried about radon health risks were older and/or men, while negative emotional responses to awareness information were reported more by younger people, women and/or parents. This highlights the importance of developing targeted demographic messaging to create effective radon exposure prevention strategies.
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".