Bibliographic record
Abstract
Palliative care practitioners are now better able than ever before to ameliorate end-of-life symptom distress. What remains less developed, however, is the knowledge base and skill set necessary to recognize, assess, and compassionately address the psychosocial, existential, and spiritual aspects of the patient's dying experience. This review provides an overview of these areas, focusing primarily on empirical data that has examined these issues. A brief overview of psychiatric challenges in end-of-life care is complemented with a list of resources for readers wishing to explore this area more extensively. The experience of spiritual or existential suffering toward the end of life is explored, with an examination of the conceptual correlates of suffering. These correlates include: hopelessness, burden to others, loss of sense of dignity, and desire for death or loss of will to live. An empirically-derived model of dignity is described in some detail, with practical examples of diagnostic questions and therapeutic interventions to preserve dignity. Other interventions to reduce existential or spiritual suffering are described and evidence of their efficacy is presented. The author concludes that palliative care must continue to develop compassionate, individually tailored, and effective responses to the mounting vulnerability and increasingly difficult physical, psychosocial, and spiritual challenges facing persons nearing the end of life.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".