Impact of home remediation and household education on indoor air quality, respiratory visits and symptoms in Alaska Native children
Bibliographic record
Abstract
Alaska Native children experience high rates of lower respiratory tract infections (LRTIs) and lung conditions, which are associated with substandard indoor air quality (IAQ). We conducted an intervention of home remediation and education to assess the impact on IAQ, respiratory symptoms and LRTI visits. We enrolled households of children 1–12 years of age with lung conditions. Home remediation included improving ventilation and replacing leaky woodstoves. We provided education about IAQ and respiratory health. We monitored indoor airborne particles (PM2.5), CO2, relative humidity and volatile organic compounds (VOCs), and interviewed caregivers about children’s symptoms before, and for 1 year after intervention. We evaluated the association between children’s respiratory visits, symptoms and IAQ indicators using multiple logistic regression. A total of 60 of 63 homes completed the study. VOCs decreased (coefficient = −0.20; p < 0.001); however, PM2.5 (coeff. = −0.010; p = 0.89) did not decrease. Burning wood for heat, VOCs and PM2.5 were associated with respiratory symptoms. After remediation, parents reported decreases in runny nose, cough between colds, wet cough, wheezing with colds, wheezing between colds and school absences. Children had an age-adjusted decrease in LRTI visits (coefficient = −0.33; p = 0.028). Home remediation and education reduced respiratory symptoms, LRTI visits and school absenteeism in children with lung conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".