The Evolution and Dissemination of the Education in Palliative and End-of-Life Care Program
Bibliographic record
Abstract
BACKGROUND: Even with growing numbers of fellowship-trained palliative care providers, primary palliative care knowledge and skills are needed to meet the national demands for palliative care. The Education in Palliative and End-of-Life Care (EPEC) Program has been one model of training clinicians in primary palliative care skills. In our second 5 years of development and dissemination, we have focused on adapting EPEC to different specialties. OBJECTIVE: Our aim was to describe the development of EPEC adaptations and document the dissemination of our curriculum. METHODS: The study design was a survey of EPEC trainers and documentation of other dissemination efforts via literature and Internet searches. Our subjects were all EPEC trainers and end-learners of our curriculum. We measured dissemination and teaching efforts by our trainers and evidence of EPEC use via literature and EPEC's searches. RESULTS: In Internet second 5 years of active development, teaching, and dissemination, we have created five major adaptations (EPEC-Oncology, EPEC-Oncology-Canada, EPEC-Emergency Medicine, EPEC-India, and EPEC for Veterans) and trained more than 1000 trainers. Through the efforts of these Trainers and our online dissemination, more than 74,000 reported end-learners have been taught parts of the EPEC curriculum. In addition, we discovered multiple medical school courses, continuing medical education (CME), courses and specialty guidelines that have incorporated material from EPEC. CONCLUSIONS: In its second 5 years, EPEC remains a robust platform for adaptation to new specialties and for dissemination of primary palliative care knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".