The Italian validation of the Death and Dying Distress Scale
Bibliographic record
Abstract
OBJECTIVES: Death anxiety (DA), a condition characterized by fear, angst, or panic related to the awareness of one's own death, is commonly observed in advanced cancer patients. The aim of this study was to examine the psychometric properties of the Italian version of the Death and Dying Distress Scale (DADDS-IT) in a sample of patients with advanced cancer. METHODS: The sample included 200 Italian advanced cancer patients meeting eligibility criteria to access palliative care. Patients' levels of DA were assessed by using the DADDS-IT, while the levels of depression, anxiety, demoralization, spiritual well-being, and symptom burden were assessed using the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Demoralization Scale, the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale, and the Edmonton Symptom Assessment System, respectively; Karnofsky Performance Status was used to measure functional impairment. Confirmatory factor analyses (CFA) of previous structures and exploratory factor analyses (EFA) were conducted. RESULTS: = 0.73), accounting for the 77.1% of the variance. Dying subscore was higher in hospice patients than in those recruited in medical wards. SIGNIFICANCE OF RESULTS: The present study provides further evidence that DA is a condition that deserves attention and that DADDS-IT shows good psychometric properties to support its use in research and clinical settings.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".