Facilitators and barriers to general practitioner and general practice nurse participation in end-of-life care: systematic review
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. To enhance primary EoLC, the facilitators and barriers to their provision need to be understood. OBJECTIVE: To provide a comprehensive description of the facilitators and barriers to GP and GPN provision of PC or EoLC. METHOD: Systematic literature review. Data included papers (2000 to 2017) sought from Medline, PsycInfo, Embase, Joanna Briggs Institute and Cochrane databases. RESULTS: From 6209 journal articles, 62 reviewed papers reported the GP's and GPN's role in EoLC or PC practice. Six themes emerged: patient factors; personal GP factors; general practice factors; relational factors; co-ordination of care; availability of services. Four specific settings were identified: aged care facilities, out-of-hours care and resource-constrained settings (rural, and low-income and middle-income countries). Most GPs provide EoLC to some extent, with greater professional experience leading to increased comfort in performing this form of care. The organisation of primary care at practice, local and national level impose numerous structural barriers that impede more significant involvement. There are potential gaps in service provision where GPNs may provide significant input, but there is a paucity of studies describing GPN routine involvement in EoLC. CONCLUSIONS: While primary care practitioners have a natural role to play in EoLC, significant barriers exist to improved GP and GPN involvement in PC. More work is required on the role of GPNs.
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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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 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".