Bibliographic record
Abstract
During the past decade, research has examined definitions and conceptualizations of quality of dying and death in different populations. At the same time, there has been a call to clarify the distinctions between quality of dying and death and other end-of-life constructs. The purposes of this article are to (1) review research that examined definitions and conceptualizations of the quality of dying and death, (2) clarify the quality of dying and death construct and its distinction from quality of life and quality of care at the end of life, and (3) outline challenges that remain for health care professionals, researchers, and policy makers. Review of the literature revealed that the quality of dying and death construct is multidimensional, with 7 broad domains: physical experience, psychological experience, social experience, spiritual or existential experience, the nature of health care, life closure and death preparation, and the circumstances of death. The quality of dying and death is subjectively determined with numerous factors that influence its judgment, including culture, type and stage of disease, and social and professional role in the dying experience. Quality of dying and death is broader in scope than either quality of life at the end of life or quality of care at the end of life, although there is overlap among these constructs.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".