Psychosocial intervention in palliative care: What do psychologists need to know
Bibliographic record
Abstract
Emotional and existential suffering is prevalent in advanced diseases and psychologists have valuable skills to support people in this time of life. Yet, psychologists are rarely integrated in palliative care and relevant training is sparse. Being integrated in other areas of health, it is likely that we will be supporting these patients, whether integrated in a specialized team or not. This article is meant to serve psychologists, already skilled in the art and science of psychosocial intervention, who may find themselves supporting patients with advanced disease. Relevant history of palliative care is provided to elucidate palliative philosophy and approach. Evidence-based existential interventions will be reviewed. Integration of psychological models and both palliative theory and practice is provided to support palliative-appropriate case conceptualizations. Finally, case examples are provided throughout to help readers reconcile their existing practice in this domain of care.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".