Defining and determining quality in end-of-life care
Bibliographic record
Abstract
Health care providers are directing increased attention toward improving end-of-life care for patients who are dying and their families (Support Principle Investigators, 1995; Ferrell et al, 2000). Integral to the goal is determining what constitutes quality care for the dying. One of the major problems, however, in making such a determination results from the lack of consensus regarding a conceptual definition of quality. The purpose of this article is to stimulate debate and discussion on the meaning of quality in the context of providing end-of-life care. To aid in this discussion, the various perspectives of what constitutes quality in health-care practice will be discussed with special attention being paid to the delivery of quality end-of-life care. Conceptual decisions drive approaches to research method and measurement (Kristjanson, 1992), therefore an examination of the issues surrounding the measurement of quality health care in the context of care for the dying will also be explored.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".