A scoping review examining the association of housing quality and psychosocial well-being following homelessness: an ecological systems perspective
Bibliographic record
Abstract
Housing quality (HQ) has been previously associated with health and quality of life. Although HQ is an important factor in preventing homelessness, little is known about the range and breadth of this body of literature. To identify existing studies, we conducted a scoping review guided by the question: “in what ways has housing quality been associated with psychosocial well-being following homelessness in existing peer-reviewed literature?” using the framework proposed by Arksey & O'Malley and PRISMA-ScR guidelines. Our search was deployed in eight databases. A total of 713 titles and abstracts were screened following the removal of duplicates, and 32 articles were included in narrative synthesis. Six themes emerged from our data analysis of included articles: 1) quality of housing affecting well-being; 2) feeling forced to live in unsafe and poor-quality housing due to no other options; 3) HQ mediated by housing first; 4) determinants of HQ; 5) standardized measures of HQ; and 6) HQ is de-emphasized in research and practice following homelessness. Our findings demonstrate that HQ is associated with psychosocial well-being at micro, meso, exo, and macro systems levels for persons experiencing homelessness. We argue addressing HQ following homelessness is a key homelessness prevention strategy.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".