Dying at home: why not? The views of community nurses on why cancer patients may not be able to achieve a home death
Bibliographic record
Abstract
Background: It is acknowledged that between 50-75% of those with cancer and more than 50% of adults (if diagnosed with a terminal illness) would prefer to die at home. Although the introduction of initiatives such as the UK Government’s End of Life Care Programme is intended to promote patient choice at this time, more than a quarter of those with a terminal illness do not die at home. A gap clearly exists between patients’ preferences and their actual place of death that is poorly understood. Various influencing factors have been suggested including local out of hours services, a cancer diagnosis and the ability of families to care. This study sought to explore the topic from the perspective of community nurses, in an attempt to identify factors locally, which might result in a change in decision away from a preferred home death for people with cancer. Methodology: As the study sought to explore personal perspectives on the topic, a qualitative methodology was adopted. Data collection was through focus groups, which enabled group discussion and interaction, and allowed participants to use their own frames of reference. A purposive sample of community Macmillan nurses and district nurses who could draw on examples from their practice in a discussion around place of death in cancer were invited to take part. Nineteen nurses from two primary care trusts in the north west of England participated in two audio taped focus groups. Data were analysed for emerging themes using thematic analysis. Results and Discussion: Two main themes emerged, carer breakdown and service provision. However contributory factors were identified including: delays in provision of services, unrealistic expectations of patients, carers and hospital staff, illness duration and patients’ perceptions of their carer’s abilities. This paper discusses the results and explores potential reasons for the findings.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| 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".