Relocating into long-term care from hospital: a comparison of two systems
Bibliographic record
Abstract
Transitioning into a long-term care home has been described as a difficult experience for both patients and families. This is particularly true of transitions from hospital where pressure to make immediate decisions is increased, choices are limited and policies resulting in multiple moves have been implemented. This study quantitatively analyzed hospital data in a Quebec based hospital prior to and following the implementation of a new policy requiring all patients deemed medically stable to be discharged to a temporary facility prior to their permanent relocation. More specifically secondary data analysis was used to explore (1) if patients' decisions to opt out of the public system increased when the new transitional system was implemented and (2) if rates of return through the ER increased under the new system. Data from 321 patients were analyzed including 161 patients under the old system (LTC) and 160 patients under the new system (PHPE). Analysis of the data revealed that more patients followed the public process in the new system and that there was a trend towards increased visits to the ER by patients in the new system. As provincial governments across Canada continue to adopt policies that force multiple moves from hospital more research is needed on the impact of such policies on patients' and families' experiences of care, physical and mental health and health care utilization.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".