Definitions of Palliative Care Terms: A Consensus-Oriented Decision-Making Process
Bibliographic record
Abstract
Background: Lack of consistency in palliative care language can serve as barriers when designing, delivering, and accessing high-quality palliative care services. Objective: To develop a consensus-driven and evidence-based palliative care glossary for the Health Standards Organization Palliative Care Services National Standard of Canada (CAN/HSO 13001:2020). Design: Content analysis of the Palliative Care Services standard was used to refine a list of terms. Environmental scan and rapid review were used for identification of concepts and definitions. Two meetings of consultation based on the modified Delphi approach took place among a working committee consisting of 12 health care providers, administrators, academics, and patient/family representatives. Results: Palliative approach to care, quality of life, pain and symptom management, caregivers, palliative care, life-limiting illness, and serious illness were defined by modification/adoption of existing definitions. Conclusion: A glossary of key palliative care terms was developed and included in the HSO Palliative Care Services standard, which will facilitate communication using consistent language across care settings.
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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 | 0.460 | 0.423 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.015 | 0.032 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".