Strengthening care for adults with palliative care needs in high-income rural communities: a global policy environmental scan
Bibliographic record
Abstract
BACKGROUND: Almost 45% of the world's population resides in rural locations. Despite this, access to best evidence-based palliative care is variable. Reforming and optimising rural palliative care is dependent upon positive public policy. AIM: To map country- and jurisdiction- level policy against the elements of care required to optimise rural palliative care provision in high-income countries. DESIGN AND DATA SOURCES: An environmental scan of policies denoting actions informing rural palliative care access and delivery in high-income countries, performed using a modified version of Khalil and colleagues' five-stage scoping review methodology. Grey literature was searched in November 2024 across Australia, Canada, Ireland, Japan, New Zealand, Norway, Finland, United Kingdom, and the United States of America. Rural specific policy actions were mapped against the World Health Organization's Innovative Care for Chronic Conditions Framework (ICCCF). RESULTS: Of 3809 records screened, eight country-level and eight jurisdiction-level palliative care policies denoting 113 rural palliative care specific actions across 13 of 18 WHO ICCCF elements of care were identified. Over 90% of actions addressed macro-(n = 52, 47%) or meso- level (n = 50; 44%) elements, and two-thirds addressed five sub-categories: 1) Build workforce capacity; 2) Develop rural specific teams, committees and positions; 3) Identify, maintain, and scale up new and/or existing rural palliative care models; 4) Increase access to integrated, seamless rural palliative care; and 5) Identify gaps in rural service provision and service planning. CONCLUSIONS: While there is a wide spread of actions across macro- and meso- level WHO ICCCF elements, there is limited focus on micro- level elements, and a lack of complementary actions within documents across the three layers of care. Country-level policies are pivotal to setting the tone, while jurisdiction-level policies can further target the specific needs of rural communities within each area's unique constraints. Findings support a growing need to devise methodologies informing development and measurement of healthcare policy. Optimising rural palliative care policy demands cross-sector participation and the involvement of consumers, to co-design actions which accurately reflect the unique and nuanced rural environment and its citizens, and be capable of bridging disparities.
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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 | 0.029 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".