Quality of life of community-based palliative care clients and their caregivers
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate health-related quality of life of palliative care (PC) clients and their caregivers, at baseline and follow-up, following a referral to a community PC service. METHOD: Quality of life of clients and their caregivers was respectively measured using the McGill Quality of Life instrument (MQoL) and the Caregiver Quality of Life Cancer Index (CQoLC) instruments. Participants were recruited from June 8 to October 27, 2006. This study was undertaken in one zone of an Area Health Service in New South Wales, which has a diverse socioeconomic population. The zone covers an area of 6237 km2 and is divided into five sectors, each with a PC service, all of which participated in this study. RESULTS: Data were obtained from 49 clients and 43 caregivers at baseline, and 22 clients and 12 caregivers at 8 week follow-up. Twenty-one participants died and six moved out of the area during the study. At baseline, clients reported a low mean score for physical symptoms (3.3 ± 1.9) and a high score for support (8.7 ± 1.0). Caregivers scored a total CQoLC of 63.9 ± 21.4 and clients had a total QOL of 6.1 ± 1.3. At follow up, matched data for 22 clients and 13 caregivers demonstrated no statistical differences in quality of life. SIGNIFICANCE OF RESULTS: This study has provided evidence that health-related quality of life questionnaires show lower scores for physical health and higher scores for support, which can directly inform specific interventions targeted at the physical and support domains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".