End-of-Life care at Erie shores HealthCare
Bibliographic record
Abstract
Background: Residential palliative care in Windsor-Essex County is limited to 45 beds across three providers: Windsor Essex Hospice, Hotel-Dieu Grace Healthcare, and Journey Home Hospice. Barriers such as a shortage of palliative beds, policies excluding those in long-term care homes or homeless, and travel instability often force individuals to spend their final days in hospitals. In Ontario, about 60% of deaths occur in hospitals, costing $1,100 per day compared to $460 per day for hospice care. A palliative care program at Erie Shores HealthCare would reduce travel burden and healthcare costs and improve patient, family, and caregiver experience. Objectives: The project aims to 1) provide equitable end-of-life care in a hospital setting, 2) offer loved ones the opportunity for positive final moments, 3) ensure access to bereavement support, 4) reduce stigma around the dying process, and 5) offer holistic care including pain management, psychosocial, and spiritual support to promote dignity in dying. Methods: The project will offer social work support, palliative rooms, a comfort cart with community partners, and end-of-life materials. Family feedback will guide the project to ensure it addresses community needs. Implications: Erie Shores HealthCare's end-of-life program will offer private rooms, bereavement support, social services, and legacy-building activities to ensure comprehensive care and support for patients and their families during this difficult time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.007 |
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".