REDUCING EMERGENCY ROOM VISITS AND HOSPITAL DEATHS AT END-OF-LIFE FOR LONG-TERM CARE RESIDENTS
Bibliographic record
Abstract
Burdensome interventions and hospital use can negatively affect the quality of end-of-life (EOL) for long-term-care (LTC) residents and their families. The goal of this study was to examine Emergency Department (ED) use at EOL and hospital deaths for LTC residents, and explore with LTC staff ways to minimize hospital use. This study used a mixed methods approach. Chart audits were conducted in four LTC homes in southern Ontario to capture trends in hospital use over a one-year period for the following indicators: (1) resident deaths at hospital versus LTC home; (2) ED visit in the last year, month, and week of life; (3) average number of ED visits/resident; (4) planned versus unplanned ED visits; (5) ED visits that became hospital admissions. These chart audit findings were presented to staff to raise awareness and stimulate reflections on local factors affecting hospital use at EOL. All deliberations were transcribed and thematically analyzed. Chart audits revealed that 59% of residents across sites visited ED during the last month of life and 26% of resident deaths occurred in hospital. Staff expressed surprise at the amount of hospital use during EOL. Reflections suggested that clinical expertise, comfort with EOL communication, clinical resources and family availability for EOL decision-making could all impact non-desirable hospital use at EOL. Staff appeared motivated to address these areas of practice following this reflective process. Localized chart data combined with group reflective opportunities can serve to raise awareness and engage staff in collective solutions to address hospital use at EOL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".