National Impact of the EPEC-Pediatrics Enhanced Train-the-Trainer Model for Delivering Education on Pediatric Palliative Care
Bibliographic record
Abstract
BACKGROUND: Lack of pediatric palliative care (PPC) training impedes successful integration of PPC principles into pediatric oncology. OBJECTIVES: -Pediatrics) curriculum on the following: (1) knowledge dissemination; (2) health professionals' knowledge; (3) practice change; and (4) quality of PPC. DESIGN: An integrated knowledge translation approach was used with pre-/posttest evaluation of care quality. Setting/Subjects/Measurements: Regional Teams of 3-6 health professionals based at 15 pediatric oncology programs in Canada became EPEC-Pediatrics Trainers who taught the curriculum to health professionals (learners) and implemented quality improvement (QI) projects. Trainers recorded the number of learners at each education session and progress on QI goals. Learners completed knowledge surveys. Care quality was assessed through surveys with a cross-sectional sample of children with cancer and their parents about symptoms, quality of life, and care quality plus reviews of deceased patients' health records. RESULTS: Seventy-two Trainers taught 3475 learners; the majority (96.7%) agreed that their PPC knowledge improved. In addition, 10/15 sites achieved practice change QI goals. The only improvements in care quality were an increased number of days from referral to PPC teams until death by a factor of 1.54 (95% confidence interval [CI] = 1.17-2.03) and from first documentation of advance care planning until death by a factor of 1.50 (95% CI = 1.06-2.11), after adjusting for background variables. CONCLUSION: While improvements in care quality were only seen in two areas, our approach was highly effective in achieving knowledge dissemination, knowledge improvement, and practice change goals.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".