Primary-level palliative care national capacity: Pallium Canada
Bibliographic record
Abstract
The need to improve access to palliative care across many settings of care for patients with cancer and non-cancer illnesses is recognised. This requires primary-level palliative care capacity, but many healthcare professionals lack core competencies in this area. Pallium Canada, a non-profit organisation, has been building primary-level palliative care at a national level since 2000, largely through its Learning Essential Approaches to Palliative Care (LEAP) education programme and its compassionate communities efforts. From 2015 to 2019, 1603 LEAP course sessions were delivered across Canada, reaching 28 123 learners from different professions, including nurses, physicians, social workers and pharmacists. This paper describes the factors that have accelerated and impeded spread and scale-up of these programmes. The need for partnerships with local, provincial and federal governments and organisations is highlighted. A social enterprise model, that involves diversifying sources of revenue to augment government funding, enhances long-term sustainability. Barriers have included Canada's geopolitical realities, including large geographical area and thirteen different healthcare systems. Some of the lessons learned and strategies that have evolved are potentially transferrable to other jurisdictions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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, 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".