Characterizing death acceptance among patients with cancer
Bibliographic record
Abstract
OBJECTIVE: Death acceptance may indicate positive adaptation in cancer patients. Little is known about what characterizes patients with different levels of death acceptance or its impact on psychological distress. We aimed to broaden the understanding of death acceptance by exploring associated demographic, medical, and psychological characteristics. METHODS: At baseline, we studied 307 mixed cancer patients attending the University Cancer Center Hamburg and a specialized lung cancer center (age M = 59.6, 69% female, 69% advanced cancer). At 1-year follow-up, 153 patients participated. We assessed death acceptance using the validated Life Attitude Profile-Revised. Patients further completed the Memorial Symptom Assessment Scale, the Demoralization Scale, the Patient Health Questionnaire, and the Generalized Anxiety Disorder Questionnaire. Statistical analyses included multinomial and hierarchical regression analyses. RESULTS: At baseline, mean death acceptance was 4.33 (standard deviation [SD] = 1.3, range 1-7). There was no change to follow-up (P = 0.26). When all variables were entered simultaneously, patients who experienced high death acceptance were more likely to be older (odds ratio [OR] = 1.04; 95% confidence interval [CI], 1.01-1.07), male (OR = 3.59; 95% CI, 1.35-9.56), widowed (OR = 3.24; 95% CI, 1.01-10.41), and diagnosed with stage IV (OR = 2.44; 95% CI, 1.27-4.71). They were less likely to be diagnosed with lung cancer (OR = 0.20; 95% CI, 0.07-0.58), and their death acceptance was lower with every month since diagnosis (OR = 0.99; 95% CI, 0.98-0.99). High death acceptance predicted lower demoralization and anxiety at follow-up but not depression. CONCLUSIONS: High death acceptance was adaptive. It predicted lower existential distress and anxiety after 1 year. Advanced cancer did not preclude death acceptance, supporting the exploration of death-related concerns in psychosocial interventions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".