Is progress being made on Canada’s palliative care framework and action plan? A survey of stakeholder perspectives
Bibliographic record
Abstract
BACKGROUND: The legalization of Medical Assistance in Dying in Canada in 2016 provided new impetus for improving palliative care. This commitment to improvement included the development of a National Palliative Care Framework and Action Plan. The purpose of this study was to understand the progress made in palliative care since 2016 from the perspective of persons working and volunteering in palliative care and compare geographic differences. METHODS: A digital survey was developed from goals identified in Canada's Palliative Care Framework and Action Plan and administered online using Qualtrics. Participants were recruited through national palliative care organizations. The survey included both quantitative survey items designed to evaluate improvements across 5 domains and 29 items and included open-ended questions about impacts, innovations, and ongoing challenges. Descriptive statistics were generated for survey domains, items, and demographic variables. Geographic differences were compared using Independent-Samples Kruskal-Wallis test. Qualitative data was analyzed inductively into themes. RESULTS: One hundred fifty surveys met inclusion criteria and were analysed. Overall, the most improvement was reported in palliative care education and the least improvement was reported in support for family caregivers. Items on which respondents reported the most improvement included healthcare provider education in palliative care, advance care planning, and use of technology. Items on which respondents reported the least improvement were respite for family caregivers, access to bereavement services, and in-home support for family caregivers. Notably, rural participants reported more statistically significant improvements in the domains of education, access, and research and data collection than their urban counterparts. However, rural participants reported less improvement in places to die when home is not preferable. The COVID-19 pandemic was a significant contributor to these perceived improvements and ongoing challenges. CONCLUSION: Canada's Framework and Action Plan sets out a roadmap for improving palliative care in Canada. Participants in this survey noted significant improvements in key areas, a notable accomplishment amidst the effects of the COVID-19 pandemic. Some improvements were a result of greater use of distance technology. Further leveraging these improvements will make an important contribution to solving some of the rural and remote palliative care issues that have arisen from Canada's unique geography.
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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 | 0.001 |
| 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".