Housing characteristics and indoor air quality in households of Alaska Native children with chronic lung conditions
Bibliographic record
Abstract
Alaska Native children experience high rates of respiratory infections and conditions. Household crowding, indoor smoke, lack of piped water, and poverty have been associated with respiratory infections. We describe the baseline household characteristics of children with severe or chronic lung disease participating in a 2012–2015 indoor air study. We monitored indoor PM2.5, CO2, relative humidity %, temperature, and VOCs and interviewed caregivers about children's respiratory symptoms. We evaluated the association between reported children's respiratory symptoms and indoor air quality indicators using multiple logistic regression analysis. Compared with general US households, study households were more likely overcrowded 73% (62%–82%) vs 3.2% (3.1%–3.3%); had higher woodstove use as primary heat source 16% (9%–25%) vs 2.1% (2.0%–2.2%); and higher proportion of children in a household with a smoker 49% (38%–60%) vs 26.2% (25.5%–26.8%). Median PM2.5 was 33 μg/m3. Median CO2 was 1401 ppm. VOCs were detectable in all homes. VOCs, smoker, primary wood heat, and PM2.5>25 μg/m3 were associated with higher risk for cough between colds; VOCs were associated with higher risk for wheeze between colds and asthma diagnosis. High indoor air pollutant levels were associated with respiratory symptoms in household children, likely related to overcrowding, poor ventilation, woodstove use, and tobacco smoke.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".