Where the Dying Live: A Systematic Review of Determinants of Place of End-of-Life Cancer Care
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To describe the determinants of place of end-of-life (EOL) care for patients with cancer. DATA SOURCES: A systematic literature review of primary research studies (1997-2007) was conducted. Studies that investigated place of EOL care or identified place of EOL care in relation to outcomes were examined, their critical quality was appraised, and references were mapped. DATA SYNTHESIS: Of the 735 articles identified, 39 (representing 33 studies) met inclusion criteria. Two main research designs emerged: large-scale epidemiologic reports and smaller descriptive studies. Findings suggest that factors related to the disease, the individual, and the care and social environment influence place of EOL care for patients with cancer. Social support, healthcare inputs (from services and programs and healthcare provider contact), and patient preferences were the most important factors. CONCLUSIONS: Most patients with terminal cancer prefer home palliation; however, most die in an institution. The reasons are complex, with various determinants influencing decisions regarding place of EOL care. IMPLICATIONS FOR NURSING: Findings may highlight evidence-based interventions to assist patients and families facing decisions regarding place of EOL care. A clearer understanding of factors that influence place of EOL care for patients with cancer could enhance healthcare policy and guide needs-based modifications of the healthcare system.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".