The effect of psychosocial and healthcare interventions on hospitalisation in people with dementia: an umbrella review
Bibliographic record
Abstract
Abstract Objective To evaluate the efficacy of psychosocial and healthcare interventions on reducing hospitalisation in people with dementia (PwD). Design Umbrella review of existing systematic reviews and meta-analyses. Search strategy MEDLINE, Embase, Cochrane, and CINAHL were searched until September 2025 for relevant keywords and medical subject headings. Inclusion criteria Peer-reviewed systematic reviews with or without meta-analyses examining the effect of any psychosocial or healthcare intervention compared to a suitable control group on any measure of hospitalisation in people with dementia. Methods Two reviewers assessed eligibility, and eligible papers were grouped into categories and appraised by two reviewers using AMSTAR (A MeaSurement Tool to Assess systematic Reviews) version 2. We assessed certainty of evidence for each intervention type using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Results The search identified 25 systematic reviews, comprising 77 unique primary studies (47 randomised control trials, 30 non-randomised studies of interventions), totalling 1,483,077 participants. There was high-certainty evidence that case management and exercise programmes had no effect on hospitalisation of PwD. There was low certainty evidence that advance care planning (ACP) reduced hospital admissions. Moderate certainty evidence from one study suggested that including clinical pharmacists in multidisciplinary teams (MDTs) reduced medication-related hospital admissions. Conclusions The current evidence for psychosocial and healthcare interventions in reducing hospitalisation in dementia is insufficient to make strong recommendations. ACPs show promise in reducing hospitalisations, and clinical pharmacists in MDTs may reduce medication-related readmissions. As evidence-based recommendations are needed to reduce the burden of dementia-related hospitalisations, more robust research is needed, especially high-quality randomised control trials with greater detail and standardised checklists for interventions and outcomes to reduce heterogeneity and establish more confident clinical recommendations. Registration PROSPERO 2024 CRD42024604296
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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 | 0.026 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".