General practice nurses and physicians and end of life: a systematic review of models of care
Bibliographic record
Abstract
BACKGROUND: General practitioners (GPs) and general practice nurses (GPNs) face increasing demands to provide palliative care (PC) or end-of-life care (EoLC) as the population ages. In order to maximise the impact of GPs and GPNs, the impact of different models of care that have been developed to support their practice of EoLC needs to be understood. OBJECTIVE: To examine published models of EoLC that incorporate or support GP and GPN practice, and their impact on patients, families and the health system. METHOD: Systematic literature review. Data included papers (2000 to 2017) sought from Medline, Psychinfo, Embase, Joanna Briggs Institute and Cochrane databases. RESULTS: From 6209 journal articles, 13 papers reported models of care supporting the GP and GPN's role in EoLC or PC practice. Services and guidelines for clinical issues have mixed impact on improving symptoms, but improved adherence to clinical guidelines. National Frameworks facilitated patients being able to die in their preferred place. A single specialist PC-GP case conference reduced hospitalisations, better maintained functional capacity and improved quality of life parameters in both patients with cancer and without cancer. No studies examined models of care aimed at supporting GPNs. CONCLUSIONS: Primary care practitioners have a natural role to play in EoLC, and most patient and health system outcomes are substantially improved with their involvement. Successful integrative models need to be tested, particularly in non-malignant diseases. Such models need to be explored further. More work is required on the role of GPNs and how to support them in this role.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".