Housing Design and Modifications for Individuals With Intellectual and Developmental Disabilities and Complex Behavioral Needs: Scoping Review
Bibliographic record
Abstract
Abstract Background Behavioral challenges exhibited by individuals with IDD can signal a poor person–environment relationship. There remains limited understanding about the physical characteristics of successful housing for this population. This article summarizes research on housing design for individuals with IDD who engage in behaviors that challenge. Specific Aims Original, peer‐reviewed research on the physical environment of housing was reviewed to determine the characteristics that can be modified to meet the needs of individuals with IDD who engage in behaviors that challenge. Method Electronic databases and reference lists of relevant publications were searched for peer reviewed empirical research related to housing design for behaviors that challenge. Two reviewers independently applied inclusion criteria to identify studies. Content analysis identified housing features. Findings Fourteen studies were identified that described inadequate and successful housing characteristics. Elements such as location, layout, safety, stimulation, and homelikeness were reported to contribute to successful housing. Discussion Design of the physical environment has important policy and practice implications for supported housing that addresses behaviors that challenge. The development of design tools, guidelines, and personalized housing for this population is 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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".