Developing a Palliative Care Competency Framework for Health Professionals and Volunteers: The Nova Scotian Experience
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In 2014, Nova Scotia released a provincial palliative care strategy and implementation working groups were established. The Capacity Building and Practice Change Working Group, comprised of health professionals, public advisors, academics, educators, and a volunteer supervisor, was asked to select palliative care education programs for health professionals and volunteers. The first step in achieving this mandate was to establish competencies for health professionals and volunteers caring for patients with life-limiting illness and their families and those specializing in palliative care. METHODS: In 2015, a literature search for palliative care competencies and an environmental scan of related education programs were conducted. The Irish Palliative Care Competence Framework serves as the foundation of the Nova Scotia Palliative Care Competency Framework. Additional disciplines and competencies were added and any competencies not specific to palliative care were removed. To highlight interprofessional practice, the framework illustrates shared and discipline-specific competencies. Stakeholders were asked to validate the framework and map the competencies to educational programs. Numerous rounds of review refined the framework. RESULTS: The framework includes competencies for 22 disciplines, 9 nursing specialties, and 4 physician specialties. CONCLUSIONS: The framework, released in 2017, and the selection and implementation of education programs were a significant undertaking. The framework will support the implementation of the Nova Scotia Integrated Palliative Care Strategy, enhance the interprofessional nature of palliative care, and guide the further implementation of education programs. Other jurisdictions have expressed considerable interest in the framework.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".