A Mixed Methods Approach to Radon Health Risk Perception of Ottawa Residents
Bibliographic record
Abstract
Background: Canadians have reason to care for indoor air quality as they spend over 90% of their time indoors. The geographic location and old houses make Ottawa residents at risk of the high level of radon exposure. Indoor radon causes more deaths than any other environmental hazards. While 55% residents are aware of the health risk, only 6% have taken action. Objective: To understand residents’ perceptions of the health risks of radon and to assess their perceived need to take action. Methods: In mixed methods approach, an online survey (N=308) with Qualtrics and semi-structured face to face qualitative interviews (N=35) were conducted with both homeowners and tenants in Ottawa. These quantified and explored residents' perception of and adaptations for the risk. Quantitative data were analyzed in SPSS and qualitative analysis was done in Atlas-Ti using a two-coder iterative content approach. Results: The majority of participants were Caucasian (77%), male (51%), homeowners (72%), and under age 65 (71%). Overall, 34% residents have some concerns about radon health risk, 12% have tested, and only 4% mitigated their homes for radon. Residents’ perception of the severity of the risk, social influence, and smoking history significantly correlate with their intention to test for radon whereas synergistic risk perception with smoking predicts the perception of severity of the risk. Three themes emerged from qualitative analyses of interviews: 1) despite the gravity of radon health risks, there are minimum government programs to inform residents. 2) residents' concern about the risk and care for family welfare motivate them to take action. 3) health communication program should tap into the emotional aspect of awareness beyond the cognitive awareness. Conclusions: There are inadequate government initiatives, and the health communication messages remained ineffective. Policy should address the shared responsibility of both government 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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".