What Determines the Quality of Life of Terminally Ill Cancer Patients from Their Own Perspective?
Bibliographic record
Abstract
BACKGROUND: Although several instruments have been developed to measure the quality of life (QOL) of palliative care patients, a rigorous research study has not specifically asked patients themselves what is important to their QOL. It is, therefore, not clear whether these instruments measure what is most important to these patients' QOL. PURPOSE: To understand the primary determinants of the QOL of palliative care patients with cancer. METHOD: The study used a qualitative paradigm. Participants were interviewed concerning what was important to their QOL. A systematic content analysis of the transcripts was carried out by all the investigators. RESULTS: Five broad domains were found to be importnat determinants of patient QOL: (1) the patient's own state, including physical and cognitive functioning, psychological state, and physical condition; (2) quality of palliative care; (3) physical environment; (4) relationships; and (5) outlook. CONCLUSIONS: Existing instruments cover many of these domains, but no single instrument includes all of the relevant content. The McGill Quality of Life Questionnaire, which we developed previously, has been revised based on these data.
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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 | 0.002 | 0.012 |
| 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 | 0.002 |
| 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".