Toward population‐based indicators of quality end‐of‐life care
Bibliographic record
Abstract
BACKGROUND: Quality indicators (QIs) are tools designed to measure and improve quality of care. The objective of this study was to assess stakeholder acceptability of QIs of end-of-life (EOL) care that potentially were measurable from population-based administrative health databases. METHODS: After a literature review, the authors identified 19 candidate QIs that potentially were measurable through administrative databases. A modified Delphi methodology, consisting of multidisciplinary panels of cancer care health professionals in Nova Scotia and Ontario, was used to assess agreement on acceptable QIs of EOL care (n = 21 professionals; 2 panels per province). Focus group methodology was used to assess acceptability among patients with metastatic breast cancer (n = 16 patients; 2 groups per province) and bereaved family caregivers of women who had died of metastatic breast cancer (n = 8 caregivers; 1 group per province). All sessions were audiotaped, transcribed verbatim, and audited, and thematic analyses were conducted. RESULTS: Through the Delphi panels, 10 QIs and 2 QI subsections were identified as acceptable indicators of quality EOL care, including those related to pain and symptom management, access to care, palliative care, and emergency room visits. When Delphi panelists did not agree, the principal reasons were patient preferences, variation in local resources, and benchmarking. In the focus groups, patients and family caregivers also highlighted the need to consider preferences and local resources when examining quality EOL care. CONCLUSIONS: The findings of this study should be considered when developing quality monitoring systems. QIs will be most useful when stakeholders perceive them as measuring quality care.
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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 | 0.182 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".