Health, housing and covid-19: Public health efforts are vital in reducing gaps, but change can’t stop there
Bibliographic record
Abstract
Marc Lalonde’s insights on the social determinants of health – the conditions in which people live, work and play” – have drawn major attention to housing over the decades, inspiring a wide range of policy decisions that has impacted the way we live today. However, in the wake of the COVID-19 pandemic, outbreaks in historically disadvantaged populations (e.g. black/ethnic, low-income communities etc.) reveal that there is more work to do. According to intervention studies, housing is a linking factor between “upstream” socioeconomic determinants and “downstream” interventions that help reduce health disparities. For this reason, housing is not separate from public health: housing is public health. As a rise in COVID cases and extended lockdowns continue to overwhelm our country, millions will require access to a safe, healthy and affordable home for protection. But without the help of additional funding and policy reform in the social housing sector, our most disadvantaged populations will continue to bear the brunt throughout this health crisis, and one’s to come. © 2021, University of Toronto. All rights reserved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".