Indoor air quality in remote first nations communities in Ontario, Canada
Bibliographic record
Abstract
A recent study of the health of Indigenous children in four First Nations Communities in remote northwestern Ontario found that 21% of children had been admitted to hospital for respiratory infections before age 2 years. Here we report a detailed analysis of the housing conditions in these communities. We employed a variety of statistical methods, including linear regression, mixed models, and logistic regression, to assess the correlations between housing conditions and loadings of biocontaminants (dust mite allergens, fungal glucan, and endotoxin) and indoor concentrations of PM2.5, CO2, benzene, and formaldehyde. The houses (n = 101) were crowded with an average of approximately 7 people. Approximately 27% of the homes had sustained CO2 concentrations above 1500 ppm. Most homes had more than one smoker. Commercial tobacco smoking and the use of non-electric heating (e.g., wood, oil) were associated with increased fine particle concentrations. Over 90% of the homes lacked working Heat Recovery Ventilators (HRVs), which was associated with increased fine particle concentrations and higher CO2. Of the 101 homes, 12 had mold damage sufficient to increase the relative risk of respiratory disease. This resulted from roof leaks, through walls or around the windows due to construction defects or lack of maintenance. A similar percentage had mold resulting from condensation on windows. Endotoxin loadings were much higher than any previous study in Canada. This work provides evidence for the need for more effort to repair existing houses and to ensure the HRVs are properly installed and maintained.
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 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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".