Aboriginal peoples’ lived experience of household overcrowding in the Kimberley and implications for research reciprocity in COVID-19 recovery
Bibliographic record
Abstract
OBJECTIVE: Household overcrowding was identified early in the COVID-19 pandemic as a risk factor increasing transmission and worsening outcomes. Nirrumbuk Environmental Health and Services designed this project to deepen understanding of Aboriginal peoples' experiences of overcrowding in social housing. METHODS: Our household survey explored overcrowding, capacity to respond to COVID-19 directives and the Canadian National Overcrowding Standard (CNOS). RESULTS: For 219 participating Aboriginal households, usual number of residents per household ranged from 1 to 14, increasing with short- and long-term visitors. 17.8% had occupants who themselves were on waiting lists for their own home. Nearly one-third of houses had three generations under one roof. 53.4% indicated isolation of COVID-19 cases as 'extremely' difficult. 33.8% indicated their community could not manage COVID-19 at scale. Overcrowding was defined by interpersonal dynamics or consequences such as plumbing blockages or conflict rather than the number or people or ratio of people to bedrooms. 64.8% welcomed CNOS to determine acceptable and healthy occupancy levels. Participants encouraged research about environmental health in Aboriginal hands. CONCLUSIONS: Cultural obligations, poverty and social housing waitlist management impose extreme demand on remote housing. CNOS relevance was endorsed but tempered by lived experience. IMPLICATIONS FOR PUBLIC HEALTH: Aboriginal-led research is directly accountable to communities through reciprocity and kinship. Nirrumbuk has already modified service planning.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".