Residents’ perceptions of radon health risks: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Radon is a high impact environmental pollutant and is the second leading cause of lung cancer in Canada. Building design, extended winter, and geographical location expose residents of Ottawa-Gatineau (the national capital region in Canada) to an increased risk. It is surprising that residents have an inadequate awareness of the risk - despite its gravity - and have taken minimum preventive actions. This study explores perceptions of radon health risk and examines the factors that enable and hinder the adoption of preventive measures among Ottawa-Gatineau residents. METHODS: We conducted semi-structured interviews with 35 residents with varying educational and income levels to inquire about their knowledge and perception of radon, and to explore their views of enablers and obstacles to taking action to reduce radon risks. Thematic, inductive data analysis was undertaken. RESULTS: The results indicate that: 1) Residents obtained information on radon from various sources that include the media, their education or occupation, their social network, and home renovation events. Limited references were made to the National Radon Program responsible for testing for radon and informing residents. 2) Awareness of radon risk varied, and the knowledge retained by some residents is insufficient to adequately protect their health. 3) Enablers for taking protective action included: having an understanding of the risk along with health consciousness; caring for family and children; knowing others who had contracted lung cancer and having financial resources. Obstacles consisted of: lack of awareness; cost; lack of home ownership; and potential difficulty in selling the house. 4) Residents attributed primary responsibility to public agencies for disseminating information, and incentivizing or mandating action through more stringent regulation. CONCLUSION: Risk perceptions are subjective, and are influenced by micro and macro level factors. Inducing protective action to reduce risk requires comprehensive interventions taking into account the dual cognitive and emotional aspects of risk perception. Future research may explore the dual aspects of risk perception and examine the contents of the risk communication message. Policy should address the responsibility of both governments and residents in tackling the issue.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".