Education in Palliative and End-of-Life Care-Pediatrics: Curriculum Use and Dissemination
Bibliographic record
Abstract
CONTEXT: The majority of seriously ill children do not have access to specialist pediatric palliative care (PPC) services nor to clinicians trained in primary PPC. The Education in Palliative and End-of-Life Care (EPEC)-Pediatrics curriculum and dissemination project was created in 2011 in response to this widespread education and training need. Since its implementation, EPEC-Pediatrics has evolved and has been disseminated worldwide. OBJECTIVES: Assessment of past EPEC-Pediatrics participants' ("Trainers") self-reported PPC knowledge, attitudes, and skills; use of the curriculum in teaching; and feedback about the program's utility and future direction. METHODS: From 2011 to 2019 survey of EPEC-Pediatrics past conference participants, using descriptive and content analyses. RESULTS: About 172 of 786 (22% response rate) EPEC-Pediatrics past participants from 59 countries across six continents completed the survey. Trainers, including Master Facilitators (MFs), used the curriculum mostly to teach interdisciplinary clinicians and reported improvement in teaching ability as well as in attitude, knowledge, and skills (AKS) in two core domains of PPC: communication and pain and symptom management. The most frequently taught modules were about multimodal management of distressing symptoms. Trainers suggested adding new content to the current curriculum and further expansion in low-medium income countries. Most (71%) reported improvements in the clinical care of children with serious illnesses at their own institutions. CONCLUSION: EPEC-Pediatrics is a successful curriculum and dissemination project that improves participants' self-reported teaching skills and AKS's in many PPC core domains. Participating clinicians not only taught and disseminated the curriculum content, they also reported improvement in the clinical care of children with serious illness.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".