Quality of end‐of‐life care for non‐cancer patients in a non‐acute hospital
Bibliographic record
Abstract
AIMS AND OBJECTIVES: Few surveys have been carried out documenting the quality of life for non-cancer patients in general district hospitals reaching the final trajectory towards death. We carried out a survey of 80 patients facing the final stages of their chronic illness as well as their carers and hospital staff. BACKGROUND: With increasing life expectancy, a large majority of patients are older, where palliative care principles for patients with cancer are equally applicable. Few surveys have been carried out documenting the quality of life for non-cancer patients in general district hospitals reaching the final trajectory towards death in terms of patients' and carers' perspective, compared with the more extensive literature for patients with cancer. DESIGN: Survey. METHODS: Assessment tools include symptom check list, geriatric depression scale, Chinese Death Anxiety Inventory and the McGill Quality of Life Questionnaire for patients; SF-12 and the Chinese cost of care index for informal carers; and the Chinese Maslach Bumout and Death Anxiety Inventories for hospital staff. RESULTS: Lower-limb weakness (92·5%), fatigue (86·2%), oedema (85%), dysphagia (58·2%) and pain (48·8%) were the most common symptoms in this group of patients. The mean Chinese Caregiver Stress Index score was 45·93 (SD 6·45) (maximum score = 80). For staff, the mean SF-12 physical component score was lower than the Hong Kong population average. CONCLUSION: The findings suggest that there is room for improvement in the quality of end-of-life care. Relevance to clinical practice. Patients in the final stages of many chronic illnesses have high prevalence of symptoms comparable to those of patients with cancer. Raising awareness and improving training for all health care professionals, formulating guidelines and care pathways and incorporating quality of care as key performance indicators are measures to improve the quality of end-of-life care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".