Enhancing end-of-life care practices on the medicine units: perspectives from nurses and families
Bibliographic record
Abstract
BACKGROUND: Death is a part of life. While most often a sombre event, opportunities exist to optimise the experience both for the dying patient and their loved ones. This is especially true in institutionalised settings, such as acute care hospitals where cure and recovery tend to be paramount. PURPOSE: To understand ways to improve end-of-life (EOL) care from the perspective of frontline nursing staff and patient and family advisors (PFAs). METHODS: We conducted focus groups with frontline nursing staff (n=14) and PFAs (n=5) to understand ways to optimise EOL care. Using a videoconference platform, one researcher used a flexible interview guide while a second researcher took field notes. These focus groups were in follow-up to a comprehensive need assessment survey as part of a programme to enhance EOL care practices on the general internal medicine units at our hospital. We used source data from deidentified audio recordings and researcher field notes. RESULTS: Five important categories regarding current EOL care practices emerged: communication among key stakeholders, assessment and management of symptoms, engagement of the palliative care team, engagement of the spiritual care team and ongoing tests and interventions at the EOL. We identified challenges specific to each respondent group as well as common challenges from both the professional and public perspectives. CONCLUSIONS: Views elicited from patients, families and nurses in this qualitative study have informed the development of strategies to enhance EOL practices in our hospital that may be useful in othercentres.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".