The Role of the Built Environment on the Quality of Life for Residents in Long-Term Care Facilities in Asia: A Scoping Review
Bibliographic record
Abstract
Background and Objectives: The quality of the built environmental features in long-term care (LTC) homes significantly influences residents' functioning (e.g., wayfinding, self-care, and social interaction) and well-being. There is limited research on the characteristics of the built environment of LTC and its influence on residents' quality of life in countries in the Asia-Pacific region (e.g., East Asia and South Asia). The older adult population in this region is expected to increase significantly in the coming decades. There are distinctive perceptions of nursing home, nursing home environments, and sociocultural norms in this geographic region. Given this context, a better understanding of the built environment of LTC facilities in this region can inform design professionals and policymakers for evidence-based decision-making. The present study undertakes a scoping review of the empirical research on the characteristics and influence of the built environment of LTC facilities on residents' quality of life in the Asia-Pacific context. Research Design and Methods: Online relevant databases were used to identify articles published 2000-2021, from which we selected 33 publications. Results: Three substantive themes were generated from the synthesis of the selected publications. These themes are (a) perceptions of nursing home, (b) impact of the built environment on residents' quality of life, and (c) assessment of the LTC built environment. Discussion and Implications: We identified research gaps in understanding the role of the built environment in nursing homes in the particular geographic context and future research directions. Five planning and design principles for LTC were derived from the synthesis of key findings to inform design professionals and policymakers.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".