A Mixed-Methods Scoping Review of Innovative Long-term Care Facility Design and Associated Outcomes
Bibliographic record
Abstract
Background and Objectives: As people live to late older adulthood, their reliance on disability supports and services increases. While these supports and services can often be provided at home, many people spend a period of their lives in long-term care, and the quality of long-term care environments is of great significance to those who make this transition and to those who support it. The objective of this study was to survey the range of design innovations in long-term care and to consider outcomes for residents, family caregivers, employees, and healthcare organizations. Research Design and Methods: To achieve these goals, we conducted a systematic scoping review and analyzed results using a convergent segregated mixed-methods approach. We summarized 75 articles on the topic of long-term care home building design by classifying structural design features and associated outcomes. Results: ). A wide range of potential positive outcomes were identified for residents, families, and staff. These outcomes included outcomes of central significance for long-term care, including improved quality of life, improved family satisfaction, and improved staff engagement in work. Discussion and Implications: Based on these results, environmental design is a critical contributor to long-term care quality.
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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 | 0.027 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".