Accelerating advanced practice palliative care competencies: An educational research initiative
Bibliographic record
Abstract
Objective: To describe the process and impact of integrating palliative care into the nursing curriculum to accelerate advanced practice palliative care competencies.Methods: Educational research was implemented at the Johns Hopkins University School of Nursing, Baltimore, MD to integrate palliative care knowledge and skills into the advanced practice nursing curriculum. Palliative care principles and skills were threaded through the curriculum and resources and contents were shared across graduate programs, faculty members, and students throughout the school. Additionally, palliative care workshops, symposium and conference were organized at the school to increase academic-practice partnerships and disseminate project progress. The initiative was evaluated using the Palliative Care Quiz for Nursing (PCQN) and Palliative-Care Self-efficacy Scale (PCSES) among faculty and students annually. Additional data on overall feedback on research activities were collected. Data were analyzed using descriptive statistics, t-tests, and analysis of variance.Results: In total 54 students, faculty, and clinicians participated in two workshops. The evaluation of workshops identified a significant improvement in confidence scores. In total, 620 faculty and students responded to the annual school-wide survey: 203 in 2019, 242 in 2020, and 175 in 2021. There were no significant changes in palliative care knowledge and confidence scores after integrating content within the curriculum. The participants agreed or strongly agreed with the overall positive feedback for the project events regarding expectation, pace, relevance, and objective.Conclusions: The academic-practice partnership could be one model for improving palliative care competencies. More educational initiatives are needed to identify the role of educational models with appropriate evaluation measures in preparing a competent palliative care workforce.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".