A framework for integrating advanced practice palliative care competencies in nursing education
Bibliographic record
Abstract
Introduction and objective: It is essential that nursing education prepares graduates to achieve the core skills required for the delivery of quality evidence-based palliative care. Hence, integrating advanced practice palliative care content into the nursing curriculum is an important priority. The objective of this study was to develop a framework and describe the process of integrating palliative care into the nursing curriculum to accelerate advanced practice palliative care competencies.Methods: Case-study methodology was used to describe an educational initiative. Through this initiative, palliative care education and skills-based resources have been integrated into the graduate nursing curriculum.Results: Varied palliative care learning resources have been incorporated and include sequential lectures, case studies, practice scenarios with identified palliative care needs, articles, and interprofessional palliative care simulations across multiple courses. To integrate palliative care content into the nursing curriculum a Framework for Integrating Palliative Care in Nursing Education was developed consisting of a cycle of five specific processes: 1) Assessment of curricular needs and goals, 2) Identification and development of resources, 3) Integration of teaching and learning activities, 4) Evaluation of content and learning, 5) Dissemination of resources and findings. A supportive organizational structure and an academic-practice partnership were identified as essential infrastructures for these processes.Conclusions: This educational initiative was vital in increasing the advanced practice nursing workforce with essential palliative care competencies to provide clinical leadership in a rapidly changing healthcare delivery system.
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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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".