Palliative care delivery across health sectors: A population-level observational study
Bibliographic record
Abstract
BACKGROUND: Little population-level information exists about the delivery of palliative care across multiple health sectors, important in providing a complete picture of current care and gaps in care. AIM: Provide a population perspective on end-of-life palliative care delivery across health sectors. DESIGN: Retrospective population-level cohort study, describing palliative care in the last year of life using linked health administrative databases. SETTING/PARTICIPANTS: All decedents in Ontario, Canada, from 1 April 2010 to 31 March 2012 ( n = 177,817). RESULTS: Across all health sectors, about half (51.9%) of all decedents received at least one record of palliative care in the last year of life. Being female, middle-aged, living in wealthier and urban neighborhoods, having cancer, and less multi-morbidity were all associated with higher odds of palliative care receipt. Among 92,276 decedents receiving palliative care, 84.9% received care in acute care hospitals. Among recipients, 35 mean days of palliative care were delivered. About half (49.1%) of all palliative care days were delivered in the last 2 months of life, and half (50.1%) had palliative care initiated in this period. Only about one-fifth of all decedents (19.3%) received end-of-life care through publicly funded home care. Less than 10% of decedents had a record of a palliative care home visit from a physician. CONCLUSION: We describe methods to capture palliative care using administrative data. Despite an estimate of overall reach (51.9%) that is higher than previous estimates, we have shown that palliative care is infrequently delivered particularly in community settings and to non-cancer patients and occurs close to death.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".