Psychosocial Interventions at the End-of-Life
Bibliographic record
Abstract
BACKGROUND: The integration of holistic and effective end-of-life (EOL) care into cancer management has increasingly become a recognized field. People living with terminal cancer and their caregivers face a unique set of emotional, spiritual, and social stressors, which may be managed by psychosocial interventions. OBJECTIVES: This study aimed to explore the types and characteristics of psychosocial interventions at the EOL for adult cancer patients and their caregivers and to identify gaps in the current literature. METHODS: A systematic search was conducted through MEDLINE (Ovid) and CINAHL from January 1, 2011, to January 31, 2021, retrieving 2453 results. A final 15 articles fulfilled the inclusion criteria, reviewed by 2 independent reviewers. Ten percent of the original articles were cross-checked against study eligibility at every stage by 2 experienced researchers. RESULTS: Most interventions reported were psychotherapies, with a predominance of meaning or legacy-related psychotherapies. Most interventions were brief, with significant caregiver involvement. Most studies were conducted in high-income, English-speaking populations. CONCLUSION: There is robust, although heterogeneous, literature on a range of psychosocial interventions at the EOL. However, inconsistencies in the terminology used surrounding EOL and means of outcome assessment made the comparison of interventions challenging. IMPLICATION FOR PRACTICE: Future studies will benefit from increased standardization of study design, EOL terminology, and outcome assessment to allow for a better comparison of intervention efficacy. There is a need for increased research in psychosocial interventions among middle- to low-income populations exploring social aspects, intimacy, and the impact of COVID-19.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".