Hope at the end of life: Making a case for hospice
Bibliographic record
Abstract
Hope is the anticipation of something better to come and an essential component of life. It is a complex notion that is fundamental to the promise of health care. Initially, hope is for cure or restoration of health but in terminal illness, when there is no longer the possibility of cure, hope rests in the knowledge and skill of the medical scientist to alter the course of disease and to prolong life. It is this expectation for renewed physical being that is the focus of every intervention. At the end of life, when science can do no more, hope endures, but the focus of hope changes. It becomes hope to find meaning in life, as it was lived, and in the time that remains. For most, however, the end of life unfolds in the scientific milieu of the hospital where the significance of redefining hope may not be considered, and many die without hope. The purpose of this article is to explore the meaning of hope, to highlight the necessity of redefining hope at the end of life, and to emphasize the importance of sanctuary in engendering hope through relationship. Hospice is proposed as a sanctuary for the final days, where the patient, family, and health professional discover a new meaning of hope through shared human experience.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.022 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 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".