Quality of Life in Terminally Ill Cancer Patients: Contributors and Content validity of Instruments
Bibliographic record
Abstract
Over the last few decades, improvement in the quality of life (QOL) of cancer patients has received a lot of attention in oncology. This study aims to further explore what factors terminally ill cancer patients report as influencing their QOL. Content analysis of 110 terminally ill cancer patients' answers to the McGill Quality of Life Questionnaire open-ended question was performed. Negative and positive factors reported by patients as having an impact on their QOL were identified then categorized into eight domains: "physical condition and symptoms," "psychological status," "existential," "relationships and support," "quality of care," "physical environment and living facilities," "hobbies and daily activities," and "finances." The "physical condition and symptoms" and "relationships and support" domains were the two most often described by participants as important to their QOL. The results support previous work identifying domains important to the QOL of terminally ill patients with cancer, but they also identify "finances" as a new domain. Based on these findings, we suggest including "finances" in QOL instruments for the terminally ill as an experimental domain.
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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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".