Psychosocial Correlates of Death Anxiety in Advanced Cancer: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Individuals living with advanced cancer commonly experience death anxiety, which refers to the distressing thoughts or feelings associated with awareness of one's mortality. Deriving an overview of existing literature on the psychological and social factors linked to death anxiety may inform conceptual models, clinical screening, and intervention strategies in oncology and palliative care. Therefore, the present scoping review was conducted to summarize the current literature on the psychosocial correlates of death anxiety among individuals with advanced cancer. METHODS: A comprehensive scoping review methodology was used following the Arksey and O'Malley framework. A literature search was conducted using four electronic databases: CINAHL, Embase, PsycInfo, and MEDLINE. Abstracts and full-text articles were screened, and relevant data were extracted and summarized. RESULTS: Sixteen studies met the inclusion criteria. Seventeen psychosocial correlates of death anxiety were identified, with depression, spiritual well-being, and attachment security representing the most frequently investigated. Four previously tested death anxiety models were also identified, two of which were designed longitudinally. CONCLUSIONS: This review provides a current summary of psychosocial factors and established models related to death anxiety in advanced cancer. Multiple psychosocial correlates should be targeted concurrently in research and clinical practice to address death anxiety. Longitudinal studies designed to test new models are especially needed to identify unique pathways contributing to death anxiety across the disease trajectory of advanced cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".