End-of-life transitions for family member on the solid tumour oncology ward: the 3 Wishes Project
Bibliographic record
Abstract
OBJECTIVES: Although death is not uncommon for hospitalised patients with cancer, there are few interventions in oncology that are designed to create a dignified, compassionate end-of-life (EOL) experience for patients and families. The 3 Wishes Project (3WP), a programme in which clinicians elicit and implement final wishes for dying patients, has been shown effective in intensive care units (ICUs) at improving the EOL experience. The objective was to initiate 3WP on an oncology ward and evaluate its effect on family member experiences of their loved one's EOL. We hypothesised that the 3WP can be implemented in the non-ICU setting and help oncological patients and their families with transition to the EOL. METHODS: When the patient's probability of dying is greater than 95%, patients and families were invited to participate in the 3WP. Wishes were elicited, implemented and categorised. Audiorecorded, semistructured interviews were conducted with family members, transcribed and analysed using content analysis. RESULTS: 175 wishes were implemented for 52 patients with cancer (average cost of US$34). The most common wish (66%) was to personalise the environment. Qualitative analysis of 11 family member interviews revealed that the 3WP facilitates three transitions at the EOL: (1) the transition from multiple admissions to the final admission, (2) the transition of a predominantly caregiver role to a family member role and (3) the transition from a focus on the present to a focus on legacy. CONCLUSION: The 3WP can be implemented on the oncology ward and enhance the EOL experience for hospitalised patients with cancer.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".