The Housing/Health Relationship: What Do We Know?
Bibliographic record
Abstract
Research into the relationship between housing and health has frequently been narrowly focused, fragmented, and of marginal practical relevance to either housing or health policy. From an extensive review of the literature, this paper reports on the current state of knowledge about the relationship between housing and health. The research falls into four distinct categories: (1) specific physical or chemical exposures; (2) specific biological exposures; (3) physical characteristics of the house; and (4) social, economic, and cultural characteristics of housing. Much of the general literature on the effects of housing on health cites previous studies and then proceeds to advocate housing policies and strategies that are aimed at improving population health. Studies providing original data on the relationship, which is the vast majority of the literature, focus on very specific physical, chemical, and biological exposures with a known or suspected effect on health within the house, or they focus on the social, economic, and cultural characteristics of the house. The mechanisms through which specific aspects of housing affect health are extremely complicated, but they do exist. Researchers have made a great deal of progress in clarifying some of these mechanisms. A large gap still exists in our knowledge about the links and pathways between housing, socio-economic status and health status.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".