Wish to die and hasten death in palliative care: a cross-sectional study factor analysis
Bibliographic record
Abstract
OBJECTIVES: The wish to die (WTD) is a complex experience sometimes accompanied by intention to hasten death. The aim of this study is to identify the predictive factors for WTD and hastening death intention (HDI) in Spanish patients with advanced illness. METHODS: This is a subanalysis of a larger cross-sectional study conducted on patients experiencing advanced illness (N=201). Sociodemographic data and data related to symptom burden (Edmonton Symptom Assessment System-Revised), depressive and anxious symptoms (Hospital Anxiety and Depression Scale), demoralisation (Spanish version of the Demoralisation Scale), perceived loss of dignity (Patient Dignity Inventory) and WTD (Assessing Frequency and Extent of Desire to Die) were collected. The analysis used univariate and multivariate logistic regression. RESULTS: The prevalence of WTD in the sample was 18%, with 8 out of 36 patients reporting HDI. The independent factors predictive of WTD were (1) knowledge of approximate prognosis (OR=4.78; 95% CI 1.20 to 10.8; p=0.001); (2) symptom burden (OR=1.05; 95% CI 1.00 to 1.09; p=0.038); and (3) the Demoralisation Scale subsection 'lack of meaning and purpose in life' (OR=1.61; 95% CI 1.30 to 1.99; p=0.000). An independent predictive factor for HDI was the Demoralisation Scale subsection 'patients' distress and coping abilities' (OR=1.47; 95% CI 1.04 to 2.08; p=0.028), while having religious beliefs was a protective factor (OR=0.13; 95% CI0.17 to 0.97; p=0.047). CONCLUSIONS: Demoralisation was found to be the only common triggering factor for WTD and HDI, although experiences share certain features. Identification of the predictive factors for WTD and HDI may contribute to their prevention and management.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| 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".