Patient awareness of prognosis, patient–family caregiver congruence on the preferred place of death, and caregiving burden of families contribute to the quality of life for terminally ill cancer patients in Taiwan
Bibliographic record
Abstract
OBJECTIVES: The main goal of end-of-life care is to achieve the best quality of life (QOL) for patients. The purpose of this study was to investigate the impact of (1) the patients' awareness of their prognosis, (2) the extent of patient-family caregiver congruence on the preferences for end-of-life care options, and (3) the perceived caregiving burden of family caregivers when they provide end-of-life care to their dying relative, on the QOL for terminally ill cancer patients in Taiwan. METHODS: A total of 1108 dyads of patient-family caregiver from 24 hospitals throughout Taiwan were one-time surveyed. Predictors of the QOL were identified by multiple regression analysis. RESULTS: Controlling for the effects of age, financial status, and symptom distress, a novel finding of this study was that the patient awareness of prognosis, patient-family caregiver congruence on the preferred place of death, and the subjective family caregiving burden had a significant impact on the QOL of Taiwanese terminally ill cancer patients. CONCLUSIONS: QOL is not only related to the unavoidable decline in physical condition and daily functioning of the dying patient but is also related to domains that, as death approaches, have the potential to show improvement through the efforts of health-care professionals, such as presenting prognostic information to optimize the patients' understanding and assists them with psychological adjustments, facilitating patient-family caregiver congruence on the end-of-life care decision regarding the place of death and lightening the caregiving burden of family caregivers.
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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 | 0.000 | 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".