Validation of the Death and Dying Distress Scale in patients with advanced cancer
Bibliographic record
Abstract
BACKGROUND: Distress about dying and death is an important clinical and research outcome in advanced cancer. A 15-item Death and Dying Distress Scale (DADDS) measure has undergone preliminary validation, but full validation in a large sample has not previously been reported. We investigated its psychometric characteristics and responsiveness to a therapeutic intervention. METHODS: This is a secondary analysis of baseline data obtained from a randomized controlled trial of psychotherapeutic intervention for patients with advanced cancer. Participants (n = 386) completed the DADDS, and measures of depression, anxiety, demoralization, quality of life (including preparation for end of life), spirituality, attachment security, and symptom burden. RESULTS: A confirmatory factor analysis revealed factors related to "Finitude" and to "Dying." Both factors showed good internal consistency (Cronbach α = 0.861-0.949). DADDS scores were significantly higher in females, those with lower income and not working. Construct validity was demonstrated by a positive correlation between DADDS and demoralization (r = 0.569-0.679), and a negative correlation with preparation for the end of life (r = -0.475 to -0.678). At 6 months, intervention participants with moderate DADDS scores at baseline reported significantly lower DADDS scores on both factors compared to usual care. CONCLUSIONS: The DADDS is a valid and brief two-factor measure that assesses distress related to the shortness of time and to the process of dying. The total and factor scores have utility in the clinical and research assessment of death-related distress in patients with advanced cancer.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".