General practitioner care of residential aged care facility residents at end of life: a systematic literature review and narrative synthesis
Bibliographic record
Abstract
OBJECTIVES: In 2023, 21% of deaths occurred in residential aged care facilities (RACFs), a setting expected to play an increasing role in palliative and end-of-life care (PEoLC). General practitioners (GPs) oversee and deliver PEoLC in residential and nursing homes, yet little is known about their practice. We conducted a systematic review of the published evidence concerning how GPs provide this care: what they do and the quality, challenges and facilitators of that care. DESIGN: Systematic review and narrative synthesis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. DATA SOURCES: Medline, Embase, CINAHL, PsycINFO, Web of Science, Scopus and NHS Evidence and grey literature via Google Scholar were searched through 9 October 2024. ELIGIBILITY CRITERIA: We included studies presenting new empirical data from qualitative, quantitative or mixed methods, were published in the English language and conducted in the UK, the European Union, Australia, New Zealand and Canada. We excluded studies with no new empirical data, discussion papers, conference abstracts, opinion pieces, study participants under 18 years old and in care settings other than RACF. DATA EXTRACTION AND SYNTHESIS: One independent reviewer used standardised methods to search and screen study titles for inclusion. This reviewer assessed all abstracts of the included papers, and a second independent reviewer screened 60% of the abstracts to validate inclusion. Risk of bias was assessed using Gough's Weight of Evidence assessment. Thematic analysis was used to describe the contents of the included papers; a narrative synthesis approach was taken to report the findings at a more conceptual level. RESULTS: The search identified 5936 titles: 35 papers were eligible and included in the synthesis. This is a nascent evidence base, lacking robust research designs and characterised by small sample sizes; the results describe the factors observed to be important in the delivery of care. Care provision is extremely variable; no models of optimal care have been put forward or tested. Challenges to care provision occur at every level of the care system. At macro level, service-level agreements and policies vary: at meso level, team-working, communication technology solutions and equipment availability vary: at micro level, GPs' interests in providing PEoLC vary as does their training. No study addresses residents' and relatives' experiences and expectations of GPs' involvement in PEoLC in RACFs. CONCLUSIONS: The limited evidence base highlights that GP care at end of life for RACF residents varies greatly, with enablers and challenges at all levels in the existing care systems. Little research has examined GP PEoLC for RACF residents in its own right; insight is derived from studies that report on this issue as an adjunct to the main focus. With national policies focused on moving more PEoLC into community settings, these knowledge deficits require urgent attention.
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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 | 0.057 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".