Pan-Canadian Framework for Palliative and End-of-Life Care Research
Bibliographic record
Abstract
Background and context: Ongoing advances have been made in the conceptualization and operationalization of palliative and end-of-life care (PEOLC) (e.g., palliative approaches to care, early identification of people who would benefit from palliative care). In addition, Canada has been a leader in PEOLC research and continues to have an internationally recognized research community. However, many Canadians continue to experience unnecessary pain and suffering at the end of life and receive care inconsistent with their goals and preferences. Within this context, the Canadian Cancer Research Alliance (CCRA) sought to develop a national research framework to guide Canada's cancer research funders in response to their strategic priority to improve the patient experience and quality of life for all cancer patients. Aim: To develop and implement a national framework and recommendations to enable funders to capitalize on existing research strengths and build capacity to address unmet needs to advance the field and broaden the scope, beyond its historical affiliation with advanced cancer, to include PEOLC for all those living and dying with life-limiting conditions. Strategy/Tactics: The framework's development was informed by multiple approaches, including: a strategic literature review; an analysis of PEOLC research funding in Canada from 2005-13; and an online survey and key informant interviews from the broader stakeholder community. Program/Policy process: A working group of CCRA member representatives and palliative care experts met regularly to provide guidance and feedback to a consultant who synthesized the data and formulated recommendations. In total, > 200 stakeholders (e.g., patients, caregivers, researchers, volunteers, practitioners, decision-makers, and policy-makers) provided input through the survey and interviews. Outcomes: The Pan-Canadian Framework for Palliative and End-of-Life Research was released March 2017. It emphasizes priorities for research funding across three broad themes: 1) Transforming models of care; 2) Patient and family centredness; and 3) Ensuring equity. The identified research priorities are underpinned by four building blocks: capacity building; knowledge, synthesis, exchange, and implementation; data access and standardization; and research network development. What was learned: Successful implementation of the framework's recommendations requires strong leadership from champions within the community. The formation of the Pan-Canadian Palliative Care Research Collaborative led by palliative care clinician-researchers, in response to the identified need for a research network, is an example of an early success resulting from release of the framework. Continued efforts are needed to ensure ongoing uptake of the framework's recommendations. CCRA members have commenced planning to identify next steps for joint action.
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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 | 0.075 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.032 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".