Nursing interventions to improve care of people with dementia in hospital: a mixed methods systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: This review will focus on the effectiveness of, and experience with, nursing interventions to improve the care of people with dementia in hospital. INTRODUCTION: Acute care for people with dementia has been identified as an area for improvement. Admission to hospital can be upsetting and difficult for people with dementia and can be associated with negative outcomes. Nurses play a significant role in shaping the experience of hospitalization and are the focus of many related interventions. INCLUSION CRITERIA: This mixed methods review will examine literature on improving acute care for people with dementia. The quantitative component will consider studies that evaluate nursing interventions to improve care of people with dementia, comparing the intervention with usual care, other therapies, or no comparator. Outcomes will include behavioral, health, and health system indicators. The qualitative component will consider studies that explore the experience of nursing interventions from the perspective of people with dementia, their family- or friend-caregivers, and nurses. METHODS: This review will be conducted in accordance with JBI methodology for mixed methods systematic reviews. Twelve databases and gray literature sources will be searched for published and unpublished studies. Titles, abstracts, and full-text selections will be screened by two or more independent reviewers and assessed for methodological validity using the standard JBI critical assessment tools. This review will follow a convergent segregated approach to data synthesis and integration. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42021230951.
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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 | 0.117 | 0.091 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.014 |
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".