A Scoping Literature Review of Dementia-Friendly Hospital Design
Bibliographic record
Abstract
Purpose: We report the findings of a knowledge synthesis research project on the topic of dementia-friendly acute care (D-FAC) design. This exploratory project systematically mapped what is known about D-FAC physical design in hospitals. We discuss our challenges in locating reportable evidence and the implications of such design for maximizing independent function while ensuring safety and harm reduction in older people living with dementia. Design and Methods: Exploratory iterative design utilizing scoping literature review methodology. Results: A total of 28 primary studies plus expert reviewers' narratives on the impact of design and architectural features on independent function of hospitalized older people with dementia were included and evaluated. Items were mapped to key design elements to describe a D-FAC environment. This scoping review project confirms the limited nature of available acute care design evidence on maximizing function. Implications: Physical design influences the usability and activity undertaken in a health care space and ultimately affects patient outcomes. Achieving safe quality hospital care for older people living with dementia is particularly challenging. Evidence of design principle effectiveness is needed that can be applied to general medical and surgical units where the bulk of older persons with and without dementia are treated.
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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 | 0.076 | 0.216 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".