Measuring the quality of dying and death in advanced cancer: Item characteristics and factor structure of the Quality of Dying and Death Questionnaire
Bibliographic record
Abstract
Background:Ensuring a good death in individuals with advanced disease is a fundamental goal of palliative care. However, the lack of a validated patient-centered measure of quality of dying and death in advanced cancer has limited quality assessments of palliative-care interventions and outcomes.Aim:To examine item characteristics and the factor structure of the Quality of Dying and Death Questionnaire in advanced cancer.Design:Cross-sectional study with pooled samples.Setting/participants:Caregivers of deceased advanced-cancer patients (N = 602; mean ages = 56.39–62.23 years), pooled from three studies involving urban hospitals, a hospice, and a community care access center in Ontario, Canada, completed the Quality of Dying and Death Questionnaire 8–10 months after patient death.Results:Psychosocial and practical item ratings demonstrated negative skewness, suggesting positive perceptions; ratings of symptoms and function were poorer. Of four models evaluated using confirmatory factor analyses, a 20-item, four-factor model, derived through exploratory factor analysis and comprising Symptoms and Functioning, Preparation for Death, Spiritual Activities, and Acceptance of Dying, demonstrated good fit and internally consistent factors (Cronbach’s α = 0.70–0.83). Multiple regression analyses indicated that quality of dying was most strongly associated with Symptoms and Functioning and that quality of death was most strongly associated with Preparation for Death (p Conclusion:A new four-factor model best characterized quality of dying and death in advanced cancer as measured by the Quality of Dying and Death Questionnaire. Future research should examine the value of adding a connectedness factor and evaluate the sensitivity of the scale to detect intervention effects across factors.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".