Housing Interventions and Control of Health-Related Chemical Agents
Bibliographic record
Abstract
Subject matter experts systematically reviewed evidence on the effectiveness of housing interventions that affect health outcomes associated with exposure to chemical agents, such as pesticides, lead, volatile organic compounds, as well as the radon gas. Particulates were also examined, and the role of ventilation on exposures was assessed. The review included both published literature and peer-reviewed reports from the US Environmental Protection Agency. Four of the 14 interventions reviewed had sufficient evidence to demonstrate their effectiveness and are ready for implementation: radon air mitigation by using active soil depressurization systems, integrated pest management to reduce exposures to pesticides, smoke-free home policies making indoor areas smoke-free (ie, no smoking allowed anywhere at any time), and residential lead hazard control. Four interventions needed more field evaluation, 3 needed formative research, and 3 either had no sufficient evidence of effectiveness or had evidence the interventions were ineffective. This evidence review shows that housing improvements are likely to help reduce radon-induced lung cancer, cardiovascular mortality related to secondhand smoke, and neurological effects from exposure to pesticides and lead paint. Investing in housing interventions may yield important savings from reduced disease and injury from avoidable exposures to chemical agents.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".