What should be measured to assess the quality of community-based palliative care? Results from a collaborative expert workshop
Bibliographic record
Abstract
OBJECTIVES: The need for palliative care (PC) will continue to increase in Canada with population aging. Many older adults prefer to "age in place" and receive care in their own homes. Currently, there is a lack of standardized quality indicators (QIs) for PC delivered in the community in Canada. METHODS: A one-day workshop collected expert opinions on what should be measured to capture quality PC. Three brainstorming sessions were focused on addressing the following questions: (1) what is important to measure to support quality PC, regardless of setting? (2) Of the identified measures, are any of special importance to care provided in the home? (3) What are the challenges, barriers, and opportunities for creating these measures? The National Consensus Project (NCP) for Quality Palliative Care framework was used as a guide to group together important comments into key themes. RESULTS: The experts identified four themes that are important for measuring quality, regardless of care setting, including access to care in the community by a multidisciplinary team, care for the individual with PC needs, support for the informal caregiver (e.g., family, friends), and symptom management for individuals with PC needs. Two additional themes were of special importance to measuring quality PC in the home, including spiritual care for individuals with PC needs and home as the preferred place of death. The challenges, barriers, and potential opportunities to these quality issues were also discussed. SIGNIFICANCE OF RESULTS: PC experts, through this collaborative process, made a substantial contribution to the creation of a standardized set of QIs for community-based PC. Having a standardized set of QIs will enable health care professionals and decision makers to target areas for improvement, implement interventions to improve the quality of care, and ultimately, optimize the health and well-being of individuals with a serious illness.
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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 | 0.255 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".