Telenursing in hospice palliative care.
Bibliographic record
Abstract
During the last months of life, many people with advanced illness will be living in their homes. Coping with changing symptoms, and ultimately preparing for death, becomes part of daily life. Whether the ill person is at home for days or for months, they depend on family or friends to be primary caregivers, supported by home-based services. However, after physician and home health offices close, many patients and their caregivers are left to cope alone. The authors describe an innovative partnership between B.C. NurseLine (a provincial tele-triage and health information call centre), the British Columbia Ministry of Health and Fraser Health Hospice Palliative Care program that created after-hours access to care for dying patients and their families in one of Canada's largest health authorities. The article outlines how information and communications technology enabled merging the capacity and expertise of B.C. NurseLine with the expertise of specialized community-based palliative care services to achieve outcomes of improved symptom management, decreased visits to emergency rooms and enhanced support for families who are caring for loved ones at home. For nurses caring for home-based patients, there are lessons to be learned about how to maximize technology to create systems that both improve access to care and are sustainable in the future.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".