Priorities for End-of-Life Care Reporting in Nursing Homes: Results From a Mixed-Methods Study
Bibliographic record
Abstract
Abstract The aim of the “Trajectories” project is to compile measures of nursing home (NH) quality to better characterize the final year of life for residents. In the first phase, we worked with various stakeholder groups to identify their priorities to focus the selection of possible outcomes relevant to end-of-life needs. Policy- and decision-makers from 5 Canadian health regions participated in an on-line, modified Delphi process to reach consensus on 3-4 measures of each burdensome symptoms and potentially inappropriate care practices. NH residents and families or care aides participated in an interview process using the Action Project Method. To date, all participants identified pain, mental health care, polypharmacy, and dyspnea as priorities. Policy- and decision-makers additionally identified infections and acute care transfers as priorities, while residents and families additionally identified mobility, cognition, and pressure ulcer care as priorities. There was general consistency across groups in terms of priorities but additional measures seemed to reflect either a system-wide or more personal perspective, depending on the source. Data collection with frontline staff and managers is on-going. Moving forward, we will use this list of prioritized outcomes to quantitatively assess the trajectories of these outcomes and associated factors, and to create a profile that allows for monitoring of end-of-life care in NHs.
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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 | 0.105 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".