Bibliographic record
Abstract
INTRODUCTION: Our practice-oriented question was ‘how can the community sector work with their local hospitals to smooth the patient's transition home from hospital’. Our answer was Home At Last, an innovative hospital-community collaboration in the Greater Toronto, Ontario, Canada. OBJECTIVE: To ensure that when seniors have had a hospital stay or emergency department visit, they are discharged in a timely manner and transitioned back to the community quickly with the right supports. THE PROGRAM: The hospital changes its discharge processes to achieve a pre-determined discharge time, usually 11:00 am, at which time transportation arrives along with a community worker who rides home with the patient and then stays to get them settled until a family member arrives home or 9:00 pm at the latest. The worker can pick up groceries and prescriptions if necessary, prepare small meals, perform light housekeeping, do laundry, and provide toileting assistance. The Home At Last Care Coordinator follows-up with the patient the next day, and designs and arranges a package of community services and follow-up visits. RESULTS: The program has enabled participating hospitals to achieve expected discharge time for patients; increase patient satisfaction with the discharge process and greater compliance with discharge plan orders. In addition, patient throughput has been improved by at least 6 hours and social readmissions have decreased.
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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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.316 | 0.062 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".