“You can feel the fresh air … ” Rural Alaska Native household perceptions of home air purifiers and health
Bibliographic record
Abstract
Alaska Native and American Indian children experience frequent respiratory illness. Indoor air quality is associated with the severity and frequency of respiratory infections in children. High efficiency particulate air (HEPA) purifiers effectively improve indoor air quality and may protect respiratory health. In 2019, the Yukon-Kuskokwim Health Corporation implemented a pilot programme that provided education and HEPA purifiers to households of children with chronic lung conditions. The team evaluated HEPA purifier acceptability and use by interviewing representatives from 11 households that participated in the pilot programme. All interviewees reported improvement in their child's health, and some believed that the health of other household members was also improved because of the HEPA purifier. Interviewees reported that the HEPA purifiers were easy to use, quiet, and not expensive to run. Five of 11 households were still using the HEPA purifier at the time of the interview, which was about three years after receipt of the unit. The most common reasons for discontinuing use were equipment failure and lack of replacement filter, suggesting that programme support could increase sustainability. Our evaluation suggests that HEPA purifiers are acceptable and feasible for use in rural Alaska Native households.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".