Evolution in Measuring the Quality of Dying
Bibliographic record
Abstract
PURPOSE: Despite multiple efforts to improve the experience for dying patients, researchers still struggle to identify appropriate outcome measures that assess patients' and families' experiences. If health care systems are to provide excellent, compassionate care to dying patients and their families, there must be a valid means of assessing the quality of those experiences and interventions to improve care. The purpose of this paper is to evaluate quality-of-life instruments currently used to assess the experiences of dying patients, and to offer a design for a next generation instrument to measure quality at the end of life. DESIGN: Sources were attained through a review of the quality of life, quality of dying, and end-of-life care literatures. The terms quality of life, quality of care, terminal care, hospice, assessment, and measurement were used singly and in combination in the MEDLINE database from 1966 to 2001. DISCUSSION: An appropriate clinical quality of dying instrument must be derived from the perspectives of end-of-life care participants and include the multiple domains of experience important to patients and families. Because dying patients are often too ill to communicate, nonresponse bias is a major problem in this population. Researchers must identify additional objective and subjective measures that clearly reflect, correspond well (or predictably) with, and serve as alternatives to patients' self-ratings. Additionally, an appropriate assessment tool must accommodate individual definitions of the quality of dying and demonstrate sensitivity to change over time.
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".