Dying at home: experience of the Verdun local community service centre.
Bibliographic record
Abstract
OBJECTIVE: To demonstrate that it is possible for a team of palliative care nurses in an urban centre to care for more than 50% of their terminally ill patients at home until they die, and that medical care delivered in the home is a determining factor in death at home versus death in a hospital. DESIGN: Analysis of place of death of terminally ill patients who died in 2012 and 2013 (N = 212) and who had been cared for by palliative care nurses, by type of medical care. SETTING: The centre local de services communautaires (CLSC) in Verdun, Que, an urban neighbourhood in southwest Montreal. PARTICIPANTS: A total of 212 terminally ill patients. MAIN OUTCOME MEASURES: Rate of deaths at home. RESULTS: Of the 212 patients cared for at home by palliative care nurses, 56.6% died at home; 62.6% received medical home care from CLSC physicians, compared with 5.0% who did not receive medical home care from any physician. CONCLUSION: Combined with a straightforward restructuring of the nursing care delivered by CLSCs, development of medical services delivered in the home would enable the more than 50% of terminally ill patients in Quebec who are cared for by CLSCs to die at home--something that most of them wish for.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".