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Record W4393277952 · doi:10.1080/22423982.2024.2335702

“You can feel the fresh air … ” Rural Alaska Native household perceptions of home air purifiers and health

2024· article· en· W4393277952 on OpenAlexaboutno aff
Madilyn Short, Jennifer Dobson, Gretchen Day, Brian Lefferts, Rosalyn Singleton, James W. Keck

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsHEPAAir purifierEnvironmental healthIndoor air qualityMedicineAir quality indexEnvironmental scienceEngineeringEnvironmental engineeringGeographyFilter (signal processing)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.396
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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