Review: The quality of dying and death: a systematic review of measures
Bibliographic record
Abstract
To determine whether modern medicine is facilitating 'good' deaths, appropriate measures of the quality of dying and death must be developed and utilized. The purpose of this paper is to identify quality of dying and death measurement tools and to determine their quality. MEDLINE (1950-2008), Healthstar (1966-2008), and CINAHL (1982-2008) were searched using keyword terms 'quality of dying/death' and 'good/bad death'. Papers that described a quality of dying and death measure or that aimed to measure the quality of dying and death were selected for review. The evaluation criteria included a description of the measure development (validated or ad hoc), the provision of a definition of quality of dying and death, an empirical basis for the measure, the incorporation of multiple domains and the subjective nature of the quality of dying and death construct, and responsiveness to change. Eighteen measures met the selection criteria. Six were published with some description of the development process and 12 were developed ad hoc. Less than half were based on an explicit definition of quality of dying and death and even fewer relied on a conceptual model that incorporated multidimensionality and subjective determination. The specified duration of the dying and death phase ranged from the last months to hours of life. Of the six published measures reviewed, the Quality of Dying and Death questionnaire (QODD) is the most widely studied and best validated. Strategies to measure the quality of dying and death are becoming increasingly rigorous. Further research is required to understand the factors influencing the ratings of the quality of dying and death.
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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 | 0.022 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".