Protecting Populations from Radon in Ontario, Canada: Translating Research to Evidence-Based Public Health Practice
Bibliographic record
Abstract
Radon is a ubiquitous public health hazard and the second leading preventable cause of lung cancer. Public Health Ontario (PHO) has been engaged in various activities to promote local level public health action on radon since 2013. This presentation will describe how these applied research projects have resulted in effective action to understand and mitigate radon exposure in Ontario.In 2013, PHO estimated that 847 lung cancer deaths per year were attributable to radon. PHO presented these results at various meetings, and in an online infographic and interactive report to highlight findings by region and identify strategies for local level public health practitioners to address radon. In 2016, PHO partnered with Cancer Care Ontario to estimate the cancer burden of environmental hazards and identified radon as the second leading carcinogen in terms of projected future cancer cases (1,310 per year). This was followed by a 1-day event for local level public health practitioners to share strategies and lessons learned in addressing radon at the local level.In late 2017, PHO conducted an online survey to identify how radon was being addressed by local level public health. There were over 100 specific activities targeting radon in one of 4 categories: responding to public concern (28%), public education activities (43%), measurement campaigns (13%), and ‘other’ (16%). Between 2013 and 2016, the number of public health authorities engaged in radon activities increased 6-fold.In early 2018, a new Ontario Healthy Environments and Climate Change Guideline was released to provide direction to local health authorities on how to approach specific public health requirements around radon (implementing public awareness initiatives and mitigation strategies). This marks the first time radon has been included in official guidance for public health practitioners in Ontario, and illustrates the provincial impact of building capacity at the local level for priority health hazards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".