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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".