What Are Staff Perceptions About Their Current Use of Emergency Departments for Long-Term Care Residents at End of Life?
Bibliographic record
Abstract
The goal of this study was to examine current rates of resident deaths, Emergency Department (ED) use within the last year of life, and hospital deaths for long-term care (LTC) residents. Using a mixed-methods approach, we compared these rates across four LTC homes in Ontario, Canada, and explored potential explanations of variations across homes to stimulate staff reflections and improve performance based on a quality improvement approach. Chart audits revealed that 59% of residents across sites visited EDs during the last month of life and 26% of resident deaths occurred in hospital. Staff expressed surprise at the amount of hospital use during end of life (EOL). Reflections suggested that clinical expertise, comfort with EOL communication, clinical resources (i.e., equipment), and family availability for EOL decision making could all affect nondesirable hospital transfers at EOL. Staff appeared motivated to address these areas of practice following this reflective process.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".