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Exploring relationships between household crowding and health in two First Nations communities

2025· article· en· W4408294098 on OpenAlexafffundabout
Shannon Hyslop, Shelley Kirychuk, Chandima Karunanayake, Wanda Martin, Donna Rennie, Lori Bradford, Vivian R. Ramsden, Brooke Thompson, Clarice Roberts, Jeremy Seeseequasis, Kathleen McMullin, Mark Fenton, Sylvia Abonyi, Punam Pahwa, James A. Dosman

Bibliographic record

VenueHealth & Place · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsSaskatchewan Health AuthorityCanadian Rural Health Research SocietyUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsCrowdingGeographySociologySocioeconomicsPsychology

Abstract

fetched live from OpenAlex

Lasting results of federal government influence for housing on-reserve include challenges with housing quantity and quality. Some First Nations communities face distressing housing shortages and household crowding. This study used a cross-sectional survey and the Canadian National Occupancy Standard definition of crowding to explore how household crowding affects health of people living on-reserve. and. First Nations Peoples from two reserve communities in Saskatchewan participated, a total of 831 individuals 18 years and older from 379 households. Household crowding and reports of respiratory diseases were high. The household crowding measure was significantly associated with chronic bronchitis. A culturally appropriate lens and more context are needed to understand household crowding on-reserve. • Many households on-reserve are over-crowded, as defined by Federal standards. • The Western developed National Occupancy Standard does not consider Indigenous cultural. • Federal housing supports have not had a cultural context and contribute to the current housing crisis on-reserve. • Household crowding was significantly associated with chronic bronchitis. • Cultural contexts are important for understanding and evaluating wellbeing and housing on-reserve.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
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 head0.461
GPT teacher head0.474
Teacher spread0.013 · 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.

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
Published2025
Admission routes3
Has abstractyes

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