Optimizing the Physical & Social Environment Within Hospitals for Patients with Dementia: a Systematic Review
Bibliographic record
Abstract
Background: As the population ages, the number of individuals living with dementia is increasing. This has implications for the health-care system, as people living with dementia are hospitalized more frequently and for longer periods. Because patients living with dementia are at increased risk for adverse events during admission, understanding how the acute care physical and social environments influence their outcomes is imperative. Thus, the objective of this review was to identify studies that modified the physical and/or social environment in acute care in order to improve care for hospitalized patients living with dementia. Methods: MEDLINE, Embase, and CINAHL databases were used to search for articles up to and including June 2021. PRISMA guidelines were followed. Two independent reviewers assessed citations and full texts against the following inclusion criteria: patients living with dementia/cognitive impairment, presence of a control group, and evidence of clinical or health systems outcomes. All published English-language articles meeting inclusion criteria were retrieved. Results: Following the database search, 12,901 citations were retrieved with 11,334 remaining after duplication removal. Of these, 15 papers met inclusion criteria. Seven studies evaluated the physical environment (e.g., addition of electronic sensor alarms and environmental cues). The remaining studies evaluated specific programs (e.g., art, music, exercise, volunteer engagement, and virtual reality). The majority of studies were low to very low quality; only three studies were RCTs. Environmental cues may initially improve wayfinding, and exercise may reduce neuropsychiatric symptoms. Conclusions: Although there are several interventions, there is a lack of high-quality evidence available to determine what exactly needs to be incorporated into acute care settings to reduce adverse outcomes for patients with dementia.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".