Accessible independent housing for people with disabilities: A scoping review of promising practices, policies and interventions
Bibliographic record
Abstract
BACKGROUND: Accessible housing is imperative to enabling independent living for many people with disabilities; yet, research consistently shows how people with disabilities often lack appropriate accessible housing and are more likely to experience unaffordable, insecure, and/or poor quality housing. Therefore, the aim of this study was to understand promising practices, policies and interventions regarding accessible independent housing for people with disabilities. METHODS: We conducted a scoping review that involved searching seven international literature databases that identified 4831 studies, 60 of which met our inclusion criteria. RESULTS: The reviewed studies involved 18 countries over a 20-year period. Our review highlighted the following key trends: (1) removing barriers to obtaining accessible housing (e.g., advocacy, builders enhancing housing supply, subsidies and financial incentives); (2) policies influencing accessible housing; (3) interventions to enhance accessible housing (i.e., home modifications, smart homes, mobile applications and other experimental devices); and (4) the impact of accessible independent housing on health and wellbeing. CONCLUSIONS: Our findings emphasize the importance of accessible housing for people with disabilities and the urgent need to advance accessible housing options.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".