Factors associated with lower quality of life among patients receiving palliative care
Bibliographic record
Abstract
AIM: This paper is a report of a study conducted to (1) assess the quality of life (QoL) and physical functioning status of patients diagnosed with advanced cancer and receiving palliative care; (2) determine if there was a statistically significant relationship between their physical functioning and QoL and (3) identify the demographic and disease-related variables related to their QoL. BACKGROUND: Achieving the best possible QoL is a major goal in palliative care. However, research findings about the relationship between QoL and demographic variables have been inconsistent. METHOD: Three hundred patients with advanced cancer were recruited from four district hospitals in Hong Kong between February 2005 and July 2006. Their QoL and physical functioning status were assessed by face-to-face interview, using the McGill Quality of Life Questionnaire (Hong Kong version) and the Palliative Performance Scale respectively. RESULTS: Participants reported reduced ambulation, inability to perform hobbies or housework, and the need for occasional assistance in self-care (mean: 64.6 out of 100, sd: 19.3, range: 20-100). QoL was fair (mean: 6.2 out of 10, sd: 1.5, range: 0.9-10). There was a weak positive association between physical functioning and QoL scores. Multiple regression analysis showed that patients who were older, female, had ever been married, or had higher physical functioning tended to have better QoL. CONCLUSION: More could be done in symptom and psychosocial management to improve patients' QoL, in particular for those who are younger, male or single, or who have lower physical functioning.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".