Dignity-conserving care: application of research findings to practice
Bibliographic record
Abstract
A central tenet of palliative care is to help people die with "dignity". The widespread use of this term presupposes that this construct is well understood from the perspective of the terminally ill, and that the factors that bolster or erode dignity are known. However, the paucity of research related to these issues suggests otherwise. Over the past 5 years, this research team, headed by Dr Chochinov, has undertaken a programme of research aimed at explicating what dignity means to those who are terminally ill, and identifying those factors that support and undermine dignity in this patient population. This article will provide a synopsis of that work, with an emphasis on the application of research findings for practice.
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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 | 0.294 | 0.416 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".