Systematic review of general practice end-of-life symptom control
Bibliographic record
Abstract
BACKGROUND: End of life care (EoLC) is a fundamental role of general practice, which will become more important as the population ages. It is essential that general practice's role and performance of at the end of life is understood in order to maximise the skills of the entire workforce. OBJECTIVE: To provide a comprehensive description of the role and performance of general practitioners (GPs) and general practice nurses (GPNs) in EoLC symptom control. METHOD: Systematic literature review of papers from 2000 to 2017 were sought from Medline, PsycINFO, Embase, Joanna Briggs Institute and Cochrane databases. RESULTS: From 6209 journal articles, 46 papers reported GP performance in symptom management. There was no reference to the performance of GPNs in any paper identified. Most GPs expressed confidence in identifying EoLC symptoms. However, they reported lack of confidence in providing EoLC at the beginning of their careers, and improvements with time in practice. They perceived emotional support as being the most important aspect of EoLC that they provide, but there were barriers to its provision. GPs felt most comfortable treating pain, and least confident with dyspnoea and depression. Observed pain management was sometimes not optimal. More formal training, particularly in the use of opioids was considered important to improve management of both pain and dyspnoea. CONCLUSIONS: It is essential that GPs receive regular education and training, and exposure to EoLC from an early stage in their careers to ensure skill and confidence. Research into the role of GPNs in symptom control needs to occur.
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".