Psychological Interventions for Patients With Advanced Disease: Implications for Oncology and Palliative Care
Bibliographic record
Abstract
A growing body of research demonstrates the feasibility and efficacy of psychological interventions for adult patients with advanced cancer. Findings from quantitative studies of psychotherapeutic interventions with primary psychological outcomes for such patients are reviewed here and recommendations for best practice are made. We consider these interventions according to three broad phases in which they are most commonly applied: soon after diagnosis of advanced cancer, when living with the disease, and at or near the end of life. Cumulative evidence from well-designed studies demonstrates the efficacy of psychosocial interventions for patients with advanced disease to relieve and prevent depression, anxiety, and distress related to dying and death, as well as to enhance the sense of meaning and preparation for end of life. Individual and couple-based interventions have been proven to be most feasible, and the development and use of tailored and validated measures has enhanced the rigor of research and clinical care. Palliative care nurses and physicians can be trained to deliver many such interventions, but a core of psychosocial clinicians, including social workers, psychologists, and psychiatrists, is usually required to train other health professionals in their delivery and to ensure their quality. Few of the interventions for which there is evidence of effectiveness have been routinely incorporated into oncology or palliative care. Advocacy on the basis of this evidence is required to build psychosocial resources in cancer treatment settings and to ensure that psychological care receives the same priority as other aspects of palliative care in oncology.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".