Application of a salutogenic design model to the architecture of low-income housing
Bibliographic record
Abstract
A strong correlation exists between inadequate housing and stress, and between stress and health. Studies have found that inadequate housing places both mental and physical stress on residents and that families and individuals living in low-income housing are often facing more stress and suffer from ill health more than those living in market housing. Salutogenesis, a concept developed by Aaron Antonovsky, focuses on what causes health rather than what causes illness. He links health with the ability to comprehend, manage and apply meaning to stress. This ability is called a Sense of Coherence. The higher the sense of coherence, the less negative the impact of stress will have on mental and physical health. It has also been shown that residents in low-income housing are often confronted with multiple social, physical, emotional and financial stressors, which can further weaken their sense of coherence. In this regard, low-income residents share a similarity with the hospital patients documented in many studies of salutogenics in healthcare, in that they face an abnormally high amount of stress and may be in situations that have weakened their sense of coherence. In the 1990s architect Alan Dilani suggested that Antonovsky’s salutogenic principles be applied to the architectural design of healthcare facilities as a means to promote health. More recently, this design model has also been applied to long-term care facilities and workplaces, however it has not yet been applied to low-income housing. Although the presence of stress is not solely due to architecture, architecture and design can either intensify or mitigate the effects of stress on health. Countless studies show a relationship between the design of our built environment and health. For example, there is a direct link between access to natural light and blood pressure, between over-crowding or chronic noise and psychological stress and between healing and nature. With over 8% of the Canadian population living in low-income housing and 3.3 million households paying more than what is considered affordable on rent (>30% of their income), addressing the health impact of low-income housing design is critical.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".