Perceptions and experiences of residents and relatives of emergencies in care homes: systematic review and meta-synthesis
Bibliographic record
Abstract
Background \nMedical emergencies in care homes, both residential and nursing care homes, are common and costly, often resulting in calls to an out-of-hours general practitioner (GP) or Emergency Medical Services (EMS). Ambulance attendance frequently results in conveyance to hospital and the concomitant high costs and risks of, often prolonged, hospitalisation. Previous reviews have focussed on various stakeholder perceptions of transfer to hospital. The aim of this systematic review was to provide a comprehensive synthesis of the perceptions and experiences of care home residents and their family members, who have experienced medical emergencies in a care home setting. \n \nMethods \nWe searched five electronic databases, supplemented with internet searches and forward and backward citation tracking from the included studies and review articles. Data were synthesised thematically following the Thomas and Harden approach. The CASP Qualitative Checklist was used to assess the quality of the included studies of this review. \n \nResults \nThe search strategy identified 6,140 citations. After removing duplicates and excluding citations based on title and abstract, and full-text screening, ten studies from four countries (Australia, Canada, UK, and US) were included in the review and meta-synthesis. All included studies were considered of acceptable quality. Through an iterative approach, we developed six analytical themes. 1. Infrastructure and process requirements in care homes to prevent and address emergencies; 2. The decision to transfer to hospital - a perfect storm; 3. Challenges of transfer and hospitalisation for older patients; 4. Good communication vital for desirable outcomes; 5. Legal, regulatory and ethical concerns; and 6. Trusting relationships enabled residents to feel safe. \n \nConclusions \nThe emergency care experience for care home residents can be enhanced by ensuring resources, staff capacity and processes for high quality care. Building trusting relationships underpinned by good communication and attention to ethical practice were also identified as important factors.
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 | 0.039 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".