The influence of symptom severity of palliative care patients on their family caregivers
Bibliographic record
Abstract
BACKGROUND: This study anlyzed whether family caregivers of patients with advanced cancer suffer impaired sleep quality, increased strain, reduced quality of life or increased care burden due to the presence and heightened intensity of symptoms in the person being cared for. METHOD: A total of 41 patient-caregiver dyads (41 caregivers and 41 patients with advanced cancer) were recruited at six primary care centres in this cross-sectional study. Data were obtained over a seven-month period. Caregiver's quality of sleep (Pittsburgh Sleep Quality Index), caregiver's quality of life (Quality of Life Family Version), caregiver strain (Caregiver Strain Index), patients' symptoms and their intensity (Edmonton Symptom Assessment System), and sociodemographic, clinical and care-related data variables were assessed. The associations were determined using non-parametric Spearman correlation. RESULTS: Total Edmonton Symptom Assessment System was significantly related to overall score of the Pittsburgh Sleep Quality Index (r = 0.365, p = 0.028), the Caregiver Strain Index (r = 0.45, p = 0.005) and total Quality of Life Family Version (r = 0.432, p = 0.009), but not to the duration of daily care (r = -0.152, p = 0.377). CONCLUSIONS: Family caregivers for patients with advanced cancer suffer negative consequences from the presence and intensity of these patients' symptoms. Therefore, optimising the control of symptoms would benefit not only the patients but also their caregivers. Thus, interventions should be designed to improve the outcomes of patient-caregiver dyads in such cases.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".