Non-pharmacological delirium detection and management interventions for informal caregivers of older people at home: A scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this proposed scoping review is to identify and map the available evidence on interventions that aim to help informal caregivers identify and/or manage delirium in an older person at home. INTRODUCTION: Delirium is a neurocognitive condition characterized by acute confusion and is associated with increased risk of morbidity and mortality. Research estimates delirium to be present in 17% of community-dwellers aged 85 and older, increasing proportionally with age to 45% in those aged 90 and older. Delirium often occurs at the onset of an older person's acute illness or exacerbation of a chronic illness (sometimes while at home) and, because of its protracted nature, usually continues after a hospital stay. Even when an older person's delirium resolves during hospitalization, they remain at risk of its recurrence after discharge home. Consequently, knowing how to detect and manage delirium is critical for informal caregivers of older people at home. However, there are no reviews focused exclusively on this topic in this setting. INCLUSION CRITERIA: The population of interest includes informal caregivers of a person aged 65+. Concepts of interest include delirium detection and/or management interventions. The context of interest is any setting where informal care is delivered, including the transition from hospital to home, in any geographical area. MATERIALS AND METHODS: The review will be conducted according to the JBI guidelines for scoping reviews. A three-step search strategy will be used to locate both published and unpublished papers in MEDLINE, Embase, CINAHL, PsycINFO, Web of Science Core Collection, ProQuest Nursing & Allied Health, SCOPUS, LILACS, and SciELO, PQD&T, NDLTD, Google Scholar and Google. No language restrictions will be placed on the review. Papers will be screened for eligibility at the title, abstract, and full text level by two independent reviewers. Data will be extracted by two independent reviewers and managed in Covidence. Any disagreements in screening or data extraction will be resolved by consensus or a third reviewer. Results will be summarised in narrative and tabular formats.
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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 | 0.062 | 0.053 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.010 |
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".