Partnering with carers in the management of delirium in general acute care settings: An integrative review
Bibliographic record
Abstract
OBJECTIVES: Delirium is a common, preventable condition. However, delirium is poorly recognised and often missed because symptoms are misinterpreted, and risk factors overlooked by health-care professionals. Carers usually have intimate knowledge about the person they care for. Therefore, they are well placed within care teams to implement delirium prevention strategies, identify symptoms and support the early diagnosis of delirium. The aim of this integrative review was to synthesise findings from the published research reporting on partnering with carers in the management of delirium in general acute care settings. METHODS: Five databases (Medline-EBSCO, PubMed, PsycINFO, ProQuest, CINAHL and SCOPUS) were searched to identify primary research regarding partnering with carers in the management of delirium in acute care settings, and results were synthesised. PRISMA guidelines were adhered to, and quality appraisal was conducted using the Mixed Methods Appraisal Tool. RESULTS: All seven studies reported that partnering with carers was a viable strategy in the management of delirium to maximise outcomes for people at risk of or experiencing delirium and that increasing carers' knowledge of delirium was key. The synthesis of findings also identified two themes: Increasing knowledge and Effective partnerships. CONCLUSIONS: A collaborative approach to increasing carers' and nurses' knowledge about the management of delirium, coupled with education on how to develop therapeutic nurse-carer relationships, is important for ongoing effective partnerships in the management of delirium. Good communication supported effective partnerships, which enabled both nurses and carers the opportunity to express their needs and concerns and negotiate collaborative involvement in the management of delirium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".