Access to palliative care in Canada
Bibliographic record
Abstract
IntroductionMany Canadians prefer to remain in the community at end-of-life, and to die at home. To do so, early and integrated palliative care (PC) is needed to provide individuals with care and support services across care settings. Measuring access to PC can help to evaluate progress over time. Objectives and ApproachThis presentation will show findings from our study on whether Canadian decedents had access to PC in the last year of life. Data from physician billings, drug claims, home care, long-term care and acute care were linked to identify decedents and PC service use. These data were also used to examine how PC may affect, or be affected by other interactions with the health system, including inpatient alternate level care days, admissions from long-term care, emergency department visits and stays in intensive care units. Gaps in data availability and quality will also be highlighted. ResultsAbout 70% of decedents were identified using administrative health data, although there were variations across jurisdictions due to differences in data availability and quality (9%-81%). For decedents identified across care settings, few received PC in the community in their last year of life, ranging from 4% in long-term care to 12% in home care. More decedents were identified as palliative in acute care (44%) but only one-third received PC in another setting despite multiple interactions with the health system in the last year of life. Those who had integrated, community-based PC were less likely to have a stay in an intensive care unit, and more likely to die in the community. However, few Canadian decedents had access to this type of care. Conclusion/ImplicationsData linkage identified opportunities for earlier integration of PC and improved care transitions. However, lack of common definitions of PC across sectors and jurisdictions, limitations in data availability and issues in PC coding were identified. Improvements in PC data are required to evaluate progress for the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".