Canada’s Evolving Medicare: End-of-Life Care
Bibliographic record
Abstract
A challenging issue in contemporary Canadian Medicare is the evolution of end-of-life care. Utilizing data from the 2016 and 2018 Health Care in Canada (HCIC) surveys, this paper compares the support and priorities of the adult public (n = 1500), health professionals (n = 400), and administrators (n = 100) regarding key components for end-of-life care just prior to and post legalization of medical assistance in dying (MAiD) in Canada. In 2016 and 2018, the public, health professionals and administrators strongly supported enhanced availability of all proposed end-of-life care options: pain management, hospice and palliative care, home care supports, and medically assisted death. In 2018, when asked which option should be top priority, the public rated enhanced medically assisted death first (32%), followed by enhanced hospice and palliative care (22%) and home care (21%). Enhanced hospice and palliative care was the top priority for health professionals (33%), while administrators rated enhanced medically assisted death first (26%). Despite legalization and increasing support for MAiD over time, health professionals have increasing fear of legal or regulatory reprisal for personal involvement in medically assisted death, ranging from 38% to 84% in 2018, versus 23% to 42% in 2016. While administrators fear doubled since 2016 (40%-84%), they felt the necessary system supports were in place to easily implement medically assisted death. Optimal management of end-of-life care is strongly supported by all stakeholders, although priorities for specific approaches vary. Over time, professionals increasingly supported MAiD but with a rising fear of legal/regulatory reprisal despite legalization. To enhance future end-of-life care patterns, continued measurement and reporting of implemented treatment options and their system supports, particularly around medically assisted death, are needed.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".