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Record W4401208529 · doi:10.1080/24745332.2024.2377187

Housing and respiratory health among Indigenous peoples in Canada

2024· article· en· W4401208529 on OpenAlexaffabout
Pamela Orr, Martha Ainslie, Linda Larcombe

Bibliographic record

VenueCanadian Journal of Respiratory Critical Care and Sleep Medicine · 2024
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEnvironmental healthIndigenousSocioeconomic statusIndoor air qualityPsychological interventionMetisGeographySocioeconomicsMedicineEcologyPopulationBiologySociology

Abstract

fetched live from OpenAlex

Indigenous (First Nations, Metis and Inuit) peoples bear a disproportionate burden of infectious and noninfectious respiratory disease in rural and urban communities in Canada. Biologic and behavioral determinants exist and have relevance, but the primary determinants are socioeconomic, environmental and political. Although Canada has recently declared a national housing crisis, crowded and poor-quality housing, or no housing at all, has been experienced by generations of Indigenous peoples. Crowding in homes or shelters increases risk of exposure and dose to infectious agents. Indoor air pollutants are due to housing defects, sources of heat and smoke, and include mold, endotoxin, mite allergens, gases and particulate matter, which in turn are associated with respiratory irritation and disease. Environmental radon is a noted risk factor for lung cancer in communities located in high risk regions. Studies have demonstrated that interventions in Indigenous communities on both the social and biomedical fronts, to improve housing and health care, result in improved well-being, of which respiratory health is only one component.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.368
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes2
Has abstractyes

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