Implementing Advance Care Planning in Palliative and End of Life Care: Perspectives from Community Nursing
Bibliographic record
Abstract
Abstract Background Advance care planning (ACP) is a priority topic within palliative care service provisions. Many healthcare professionals have a duty of care to engage patients in ACP discussions as early as possible. Family members of palliative patients are arguably best placed to encourage discussions of ACP, although often find this difficult. Therefore, community nurses immediately caring for these patients can encourage these discussions, utilising the built rapport and relationships they have with both patients and families. Despite this potential, nurses appear conflicted towards ACP discussions because of several barriers (e.g., education, time) or facilitators (e.g., relationships, patient readiness). This review was undertaken to explore these factors which influence how nurses apply ACP in the context of caring for their patients.Method To explore the perspectives of community nurses with ACP application, a scoping review was undertaken in June 2021 and the resultant database of 15 articles were thematically analysed to answer a research question: ‘What factors contribute to or hinder ACP discussion for community nurses, when providing care to palliative patients?’.Results Several ACP barriers and facilitators were identified from a nursing standpoint. Key barriers were – lack of confidence, competence, role, and prognostic uncertainty. Key facilitators identified the pertinence of the patient-practitioner relationship enabling ACP amongst nurses who had both competence and experience in ACP and/or palliative care.Conclusions This review suggests that community nurses’ perceived experience and competence is associated with implementing ACP effectively with palliative patients. Furthermore, it identified that future research is needed to develop interventions to promote ACP uptake in community settings, enabling confidence building and higher standards of palliative care via the implementation of ACPs.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".