A Mixed Methods Population Health Approach to Explore Radon-Induced Lung Cancer Risk Perception in Canada
Bibliographic record
Abstract
BACKGROUND: Radon is a predominant indoor air pollutant and second leading cause of lung cancer in radon-prone areas. Despite the gravity of the health risk, residents in Canada have inadequate perception and taken minimal protective actions. Better perception of a risk motivates people to take preventive measures. Scholarship about radon health risk perception is lacking in Canada. We applied a mixed methods population health approach to explore the determinants shaping perception and actions of a resident population in Canada. METHODS: We conducted mixed surveys (n = 557) and qualitative bilingual interviews (n = 35) with both homeowners and tenants of Ottawa-Gatineau areas. The study explored residents' risk perception and adaptations factors. Descriptive, correlational and regression analyses described and established associations between quantitative variables. Thematic, inductive analyses identified themes in the qualitative data. A mixed methods analysis triangulated both results to draw a holistic perception of the health risk. RESULTS: Residents' quantitative perceptions of radon health risk, smoking at home, synergistic risk perception, social influence and care for family were associated significantly with their intention to test for radon levels in their home, actual testing and mitigation. These results were explained further with the qualitative findings. Residents who had dual cognitive and emotional awareness of the risk were motivated enough to take preventive actions. Caring for family, knowing others who contracted lung cancer and financial capability were enablers, whereas lack of awareness and homeownership, cost of mitigation and stigma were obstacles to preventive actions. We also explored the dual subjective and objective aspects of risk perception that are influenced by micro- and macro-level determinants. CONCLUSIONS: Inducing protective action to reduce risk requires comprehensive population-level interventions considering dual perceptions of the risk that can modify the risk determinants. Future research can explore the dual aspects of risk perception and unequal distribution of the risk factors.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".