Palliative care stakeholders in Canada
Bibliographic record
Abstract
BACKGROUND: Improving access to palliative care for Canadians requires a focused collective effort towards palliative and end-of-life care advocacy and policy. However, evolution of modern palliative care in Canada has resulted in stakeholders working in isolation. Identification of stakeholders is an important step to ensure that efforts to improve palliative care are coordinated. The purpose of this analysis is to collectively identify, classify and prioritize stakeholders who made contributions to national palliative care policies in Canada. METHODS: A systematic grey literature search was conducted examining policy documents (i.e. policy reports, legislative bills, judicial court cases) in the field of palliative care, end-of-life care and medical assistance in dying, at the national level, over the last two decades. Organizations' names were extracted directly or derived from individuals' affiliations. We then classified stakeholders using an adapted classification approach and developed an algorithm to prioritize their contributions towards the publication of these documents. RESULTS: Over 800 organizations contributed to 115 documents (41 policy reports, 11 legislative, 63 judicial). Discussions regarding national palliative care policy over the last two decades peaked in 2016. Stakeholder organizations contributing to national palliative care policy conversations throughout this period were classified into six types broadly representative of society. The ranking algorithm identified the top 200 prioritized stakeholder organizations. CONCLUSIONS: Stakeholders from various societal sectors have contributed to national palliative care conversions over the past two decades; however, not all the stakeholder organizations engaged to the same extent. The information is useful when a need arises for increased collaboration between stakeholders and can be a starting point for developing more effective engagement strategies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".