Permanent Supportive Housing Design Characteristics Associated with the Mental Health of Formerly Homeless Adults in the U.S. and Canada: An Integrative Review
Bibliographic record
Abstract
The built environment directly and indirectly affects mental health, especially for people transitioning from long-term homelessness to permanent supportive housing (PSH) who often experience co-occurring behavioral health challenges. Despite a rapid increase in PSH availability, little research examines influences of architecture and design within this context. This integrative review synthesized limited research on PSH design in the U.S. and Canada to identify built environment characteristics associated with PSH residents' mental health, highlight gaps in the literature, and prioritize future research directions. A systematic search for peer-reviewed articles was conducted using nine databases drawing from multiple disciplines including architecture, environmental psychology, interior design, psychology, psychiatry, medicine, and nursing. Seventeen articles met inclusion criteria. Study design, methodology, built environment properties, place attributes, and relevant findings were extracted and iteratively analyzed. Three domains relevant to architecture and design were identified related to home, ontological security, and trauma sensitivity; dwelling unit type, privacy, control, safety, housing quality and location, and access to amenities; and shared common space. Integrative review results emphasize the potential of architecture and design to contribute to improved built environment quality and mental health outcomes among PSH residents. Methodological limitations and directions for future research are also discussed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".