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 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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".